{"markup":"\u003C?xml version=\u00221.0\u0022 encoding=\u0022UTF-8\u0022 ?\u003E\n \u003Chtml version=\u0022HTML+RDFa+MathML 1.1\u0022\n xmlns:content=\u0022http:\/\/purl.org\/rss\/1.0\/modules\/content\/\u0022\n xmlns:dc=\u0022http:\/\/purl.org\/dc\/terms\/\u0022\n xmlns:foaf=\u0022http:\/\/xmlns.com\/foaf\/0.1\/\u0022\n xmlns:og=\u0022http:\/\/ogp.me\/ns#\u0022\n xmlns:rdfs=\u0022http:\/\/www.w3.org\/2000\/01\/rdf-schema#\u0022\n xmlns:sioc=\u0022http:\/\/rdfs.org\/sioc\/ns#\u0022\n xmlns:sioct=\u0022http:\/\/rdfs.org\/sioc\/types#\u0022\n xmlns:skos=\u0022http:\/\/www.w3.org\/2004\/02\/skos\/core#\u0022\n xmlns:xsd=\u0022http:\/\/www.w3.org\/2001\/XMLSchema#\u0022\n xmlns:mml=\u0022http:\/\/www.w3.org\/1998\/Math\/MathML\u0022\u003E\n \u003Chead\u003E\u003Cscript\u003Eif(typeof window.MathJax === \u0022undefined\u0022) window.MathJax = { menuSettings: { zoom: \u0022Click\u0022 } };\u003C\/script\u003E\n\u003Cscript src=\u0022https:\/\/www.bmj.com\/sites\/default\/files\/js\/js_rvqD3pd7j-PZ6JZdAHt2fB3_x3qj-q5uJ__00yD2B_k.js\u0022\u003E\u003C\/script\u003E\n\u003Cscript src=\u0022\/\/cdn.jsdelivr.net\/qtip2\/2.2.1\/jquery.qtip.min.js\u0022\u003E\u003C\/script\u003E\n\u003Cscript src=\u0022https:\/\/www.bmj.com\/sites\/default\/files\/js\/js_2cUKmjSRz8adYIkliNjL6xIBrL3lrCE_Kkg0tr8ZGdw.js\u0022\u003E\u003C\/script\u003E\n\u003Cscript\u003EjQuery.extend(Drupal.settings, {\u0022basePath\u0022:\u0022\\\/\u0022,\u0022pathPrefix\u0022:\u0022\u0022,\u0022highwire\u0022:{\u0022ac\u0022:{\u0022\\\/bmj\\\/358\\\/bmj.j4208.atom\u0022:{\u0022access\u0022:{\u0022full\u0022:true,\u0022reprint\u0022:true},\u0022pisa_id\u0022:\u0022\u0022,\u0022apath\u0022:\u0022\\\/bmj\\\/358\\\/bmj.j4208.atom\u0022,\u0022jcode\u0022:\u0022bmj\u0022}},\u0022processed\u0022:[\u0022highwire_math\u0022],\u0022markup\u0022:[{\u0022requested\u0022:\u0022long\u0022,\u0022variant\u0022:\u0022full-text\u0022,\u0022view\u0022:\u0022full\u0022,\u0022pisa\u0022:\u0022bmj;358\\\/sep20_12\\\/j4208\u0022}]},\u0022instances\u0022:\u0022{\\u0022highwire_abstract_tooltip\\u0022:{\\u0022content\\u0022:{\\u0022text\\u0022:\\u0022\\u0022},\\u0022style\\u0022:{\\u0022tip\\u0022:{\\u0022width\\u0022:20,\\u0022height\\u0022:20,\\u0022border\\u0022:1,\\u0022offset\\u0022:0,\\u0022corner\\u0022:true},\\u0022classes\\u0022:\\u0022qtip-custom hw-tooltip hw-abstract-tooltip qtip-shadow qtip-rounded\\u0022,\\u0022classes_custom\\u0022:\\u0022hw-tooltip hw-abstract-tooltip\\u0022},\\u0022position\\u0022:{\\u0022at\\u0022:\\u0022right center\\u0022,\\u0022my\\u0022:\\u0022left center\\u0022,\\u0022viewport\\u0022:true,\\u0022adjust\\u0022:{\\u0022method\\u0022:\\u0022shift\\u0022}},\\u0022show\\u0022:{\\u0022event\\u0022:\\u0022mouseenter click \\u0022,\\u0022solo\\u0022:true},\\u0022hide\\u0022:{\\u0022event\\u0022:\\u0022mouseleave \\u0022,\\u0022fixed\\u0022:1,\\u0022delay\\u0022:\\u0022100\\u0022}},\\u0022highwire_author_tooltip\\u0022:{\\u0022content\\u0022:{\\u0022text\\u0022:\\u0022\\u0022},\\u0022style\\u0022:{\\u0022tip\\u0022:{\\u0022width\\u0022:15,\\u0022height\\u0022:15,\\u0022border\\u0022:1,\\u0022offset\\u0022:0,\\u0022corner\\u0022:true},\\u0022classes\\u0022:\\u0022qtip-custom hw-tooltip hw-author-tooltip qtip-shadow qtip-rounded\\u0022,\\u0022classes_custom\\u0022:\\u0022hw-tooltip hw-author-tooltip\\u0022},\\u0022position\\u0022:{\\u0022at\\u0022:\\u0022top center\\u0022,\\u0022my\\u0022:\\u0022bottom center\\u0022,\\u0022viewport\\u0022:true,\\u0022adjust\\u0022:{\\u0022method\\u0022:\\u0022\\u0022}},\\u0022show\\u0022:{\\u0022event\\u0022:\\u0022mouseenter \\u0022,\\u0022solo\\u0022:true},\\u0022hide\\u0022:{\\u0022event\\u0022:\\u0022mouseleave \\u0022,\\u0022fixed\\u0022:1,\\u0022delay\\u0022:\\u0022100\\u0022}},\\u0022highwire_reflinks_tooltip\\u0022:{\\u0022content\\u0022:{\\u0022text\\u0022:\\u0022\\u0022},\\u0022style\\u0022:{\\u0022tip\\u0022:{\\u0022width\\u0022:15,\\u0022height\\u0022:15,\\u0022border\\u0022:1,\\u0022mimic\\u0022:\\u0022top center\\u0022,\\u0022offset\\u0022:0,\\u0022corner\\u0022:true},\\u0022classes\\u0022:\\u0022qtip-custom hw-tooltip hw-ref-link-tooltip qtip-shadow qtip-rounded\\u0022,\\u0022classes_custom\\u0022:\\u0022hw-tooltip hw-ref-link-tooltip\\u0022},\\u0022position\\u0022:{\\u0022at\\u0022:\\u0022bottom left\\u0022,\\u0022my\\u0022:\\u0022top left\\u0022,\\u0022viewport\\u0022:true,\\u0022adjust\\u0022:{\\u0022method\\u0022:\\u0022flip\\u0022}},\\u0022show\\u0022:{\\u0022event\\u0022:\\u0022mouseenter \\u0022,\\u0022solo\\u0022:true},\\u0022hide\\u0022:{\\u0022event\\u0022:\\u0022mouseleave \\u0022,\\u0022fixed\\u0022:1,\\u0022delay\\u0022:\\u0022100\\u0022}}}\u0022,\u0022qtipDebug\u0022:\u0022{\\u0022leaveElement\\u0022:0}\u0022,\u0022bootstrap\u0022:{\u0022anchorsFix\u0022:\u00221\u0022,\u0022anchorsSmoothScrolling\u0022:\u00221\u0022,\u0022popoverEnabled\u0022:\u00221\u0022,\u0022popoverOptions\u0022:{\u0022animation\u0022:1,\u0022html\u0022:0,\u0022placement\u0022:\u0022right\u0022,\u0022selector\u0022:\u0022\u0022,\u0022trigger\u0022:\u0022click\u0022,\u0022title\u0022:\u0022\u0022,\u0022content\u0022:\u0022\u0022,\u0022delay\u0022:0,\u0022container\u0022:\u0022body\u0022},\u0022tooltipEnabled\u0022:\u00221\u0022,\u0022tooltipOptions\u0022:{\u0022animation\u0022:1,\u0022html\u0022:0,\u0022placement\u0022:\u0022auto left\u0022,\u0022selector\u0022:\u0022\u0022,\u0022trigger\u0022:\u0022hover focus\u0022,\u0022delay\u0022:0,\u0022container\u0022:\u0022body\u0022}},\u0022ajaxPageState\u0022:{\u0022js\u0022:{\u00220\u0022:1,\u0022sites\\\/all\\\/modules\\\/highwire\\\/highwire\\\/plugins\\\/highwire_markup_process\\\/js\\\/highwire_at_symbol.js\u0022:1,\u0022\\\/\\\/cdn.jsdelivr.net\\\/qtip2\\\/2.2.1\\\/jquery.qtip.min.js\u0022:1,\u0022sites\\\/all\\\/modules\\\/highwire\\\/highwire\\\/plugins\\\/highwire_markup_process\\\/js\\\/highwire_article_reference_popup.js\u0022:1,\u0022sites\\\/all\\\/modules\\\/highwire\\\/highwire\\\/plugins\\\/highwire_markup_process\\\/js\\\/highwire_tables.js\u0022:1,\u0022sites\\\/default\\\/modules\\\/jnl-bmj\\\/jnl_bmj\\\/plugins\\\/highwire_markup_process\\\/js\\\/bmj_author_affiliates.js\u0022:1}}});\u003C\/script\u003E\n\u003Clink type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022https:\/\/www.bmj.com\/sites\/default\/files\/advagg_css\/css__dn-cpI1YtkU_iLHgA5WhlkxgYWyat_IxjF_B-WSYrpE__Ta9eNt7PPGHCfsyTneXg1ooQkRjbMt18zHVfHQYMDns__b2e6faiQ_UWIrIhhy-1_GBi9M1f1xMsmWzx8NA2PKwk.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022https:\/\/www.bmj.com\/sites\/default\/files\/advagg_css\/css__VL3TN026f9BhfsMn60oa9g1dqxN3t44ueAsjySR7zoM__gI8FKfQ0C_cA7f9XyCTyU-M649DMROWB2M5aueCwY-s__b2e6faiQ_UWIrIhhy-1_GBi9M1f1xMsmWzx8NA2PKwk.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022https:\/\/www.bmj.com\/sites\/default\/files\/advagg_css\/css__HGACIFBlu2o05y3afvqlt5wrE_5Dn6MXsexfuEpeIwg__bZRPPTxGBklJTL0kiU8ickehU_1TeXhqoRdbfYVaapM__b2e6faiQ_UWIrIhhy-1_GBi9M1f1xMsmWzx8NA2PKwk.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022\/\/cdn.jsdelivr.net\/qtip2\/2.2.1\/jquery.qtip.min.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022https:\/\/www.bmj.com\/sites\/default\/files\/advagg_css\/css__GkPSKUkdJ2bzIT9BLdob0bbh88SFHg9JxcNgbdHlbDc__qYTxeL-KKGqFiuvt1Pd7tJWAZkcDxAZN7jCKDHxZcE0__b2e6faiQ_UWIrIhhy-1_GBi9M1f1xMsmWzx8NA2PKwk.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink rel=\u0027stylesheet\u0027 type=\u0027text\/css\u0027 href=\u0027\/sites\/all\/modules\/contrib\/panels\/plugins\/layouts\/onecol\/onecol.css\u0027 \/\u003E\u003C\/head\u003E\u003Cbody\u003E\u003Cdiv class=\u0022panels-ajax-tab-panel panels-ajax-tab-panel-jnl-bmj-tab-art\u0022\u003E\u003Cdiv class=\u0022panel-display panel-1col clearfix\u0022 \u003E\n \u003Cdiv class=\u0022panel-panel panel-col\u0022\u003E\n \u003Cdiv\u003E\u003Cdiv class=\u0022panel-pane pane-highwire-markup\u0022 \u003E\n \n \n \n \u003Cdiv class=\u0022pane-content\u0022\u003E\n \u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 data-highwire-cite-ref-tooltip-instance=\u0022highwire_reflinks_tooltip\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022article fulltext-view \u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022contributors\u0022\u003E\u003Col class=\u0022contributor-list\u0022 id=\u0022contrib-group-1\u0022\u003E\u003Cli class=\u0022contributor\u0022 id=\u0022contrib-1\u0022\u003E\u003Cspan class=\u0022name\u0022\u003EJulia Hippisley-Cox\u003C\/span\u003E\u003Cspan class=\u0022contrib-role\u0022\u003E, professor of clinical epidemiology and general practice\u003C\/span\u003E, \u003C\/li\u003E\u003Cli class=\u0022last\u0022 id=\u0022contrib-2\u0022\u003E\u003Cspan class=\u0022name\u0022\u003ECarol Coupland\u003C\/span\u003E\u003Cspan class=\u0022contrib-role\u0022\u003E, professor of medical statistics in primary care\u003C\/span\u003E\u003C\/li\u003E\u003C\/ol\u003E\u003Col class=\u0022affiliation-list\u0022\u003E\u003Cli class=\u0022aff\u0022\u003E\u003Ca id=\u0022aff-1\u0022 name=\u0022aff-1\u0022\u003E\u003C\/a\u003E\u003Caddress\u003EDivision of Primary Care, University Park, University of Nottingham, Nottingham NG2 7RD, UK\u003C\/address\u003E\u003C\/li\u003E\u003C\/ol\u003E\u003Col class=\u0022corresp-list\u0022\u003E\u003Cli class=\u0022corresp\u0022 id=\u0022corresp-1\u0022\u003ECorrespondence to: J Hippisley-Cox \u003Cspan class=\u0022em-link\u0022\u003E\u003Cspan class=\u0022em-addr\u0022\u003EJulia.hippisley-cox{at}nottingham.ac.uk\u003C\/span\u003E\u003C\/span\u003E\u003C\/li\u003E\u003C\/ol\u003E\u003Cul class=\u0022history-list\u0022\u003E\u003Cli xmlns:hwp=\u0022http:\/\/schema.highwire.org\/Journal\u0022 class=\u0022accepted\u0022 hwp:start=\u00222017-09-07\u0022\u003E\u003Cspan class=\u0022accepted-label\u0022\u003EAccepted \u003C\/span\u003E7 September 2017\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022section abstract\u0022 id=\u0022abstract-1\u0022\u003E\u003Ch2\u003EAbstract\u003C\/h2\u003E\u003Cp id=\u0022p-2\u0022\u003E\u003Cstrong\u003EObjectives\u003C\/strong\u003E\u00a0To derive and validate a risk prediction equation to estimate the short term risk of death, and to develop a classification method for frailty based on risk of death and risk of unplanned hospital admission.\u003C\/p\u003E\u003Cp id=\u0022p-3\u0022\u003E\u003Cstrong\u003EDesign\u003C\/strong\u003E\u00a0Prospective open cohort study.\u003C\/p\u003E\u003Cp id=\u0022p-4\u0022\u003E\u003Cstrong\u003EParticipants\u003C\/strong\u003E\u00a0Routinely collected data from 1436 general practices contributing data to QResearch in England between 2012 and 2016. 1079 practices were used to develop the scores and a separate set of 357 practices to validate the scores. 1.47 million patients aged 65-100 years were in the derivation cohort and 0.50 million patients in the validation cohort.\u003C\/p\u003E\u003Cp id=\u0022p-5\u0022\u003E\u003Cstrong\u003EMethods\u003C\/strong\u003E\u00a0Cox proportional hazards models in the derivation cohort were used to derive separate risk equations in men and women for evaluation of the risk of death at one year. Risk factors considered were age, sex, ethnicity, deprivation, smoking status, alcohol intake, body mass index, medical conditions, specific drugs, social factors, and results of recent investigations. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for each age and ethnic group. The new mortality equation was used in conjunction with the existing QAdmissions equation (which predicts risk of unplanned hospital admission) to classify patients into frailty groups.\u003C\/p\u003E\u003Cp id=\u0022p-6\u0022\u003E\u003Cstrong\u003EMain outcome measure\u003C\/strong\u003E\u00a0The primary outcome was all cause mortality.\u003C\/p\u003E\u003Cp id=\u0022p-7\u0022\u003E\u003Cstrong\u003EResults\u003C\/strong\u003E\u00a0During follow-up 180\u2009132 deaths were identified in the derivation cohort arising from 4.39 million person years of observation. The final model included terms for age, body mass index, Townsend score, ethnic group, smoking status, alcohol intake, unplanned hospital admissions in the past 12 months, atrial fibrillation, antipsychotics, cancer, asthma or chronic obstructive pulmonary disease, living in a care home, congestive heart failure, corticosteroids, cardiovascular disease, dementia, epilepsy, learning disability, leg ulcer, chronic liver disease or pancreatitis, Parkinson\u2019s disease, poor mobility, rheumatoid arthritis, chronic kidney disease, type 1 diabetes, type 2 diabetes, venous thromboembolism, anaemia, abnormal liver function test result, high platelet count, visited doctor in the past year with either appetite loss, unexpected weight loss, or breathlessness. The model had good calibration and high levels of explained variation and discrimination. In women, the equation explained 55.6% of the variation in time to death (R\u003Csup\u003E2\u003C\/sup\u003E), and had very good discrimination\u2014the D statistic was 2.29, and Harrell\u2019s C statistic value was 0.85. The corresponding values for men were 53.1%, 2.18, and 0.84. By combining predicted risks of mortality and unplanned hospital admissions, 2.7% of patients (n=13\u2009665) were classified as severely frail, 9.4% (n=46\u2009770) as moderately frail, 43.1% (n=215\u2009253) as mildly frail, and 44.8% (n=223\u2009790) as fit.\u003C\/p\u003E\u003Cp id=\u0022p-8\u0022\u003E\u003Cstrong\u003EConclusions\u003C\/strong\u003E\u00a0We have developed new equations to predict the short term risk of death in men and women aged 65 or more, taking account of demographic, social, and clinical variables. The equations had good performance on a separate validation cohort. The QMortality equations can be used in conjunction with the QAdmissions equations, to classify patients into four frailty groups (known as QFrailty categories) to enable patients to be identified for further assessment or interventions.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv class=\u0022section intro\u0022 id=\u0022sec-1\u0022\u003E\u003Ch2 class=\u0022\u0022\u003EIntroduction\u003C\/h2\u003E\u003Cp id=\u0022p-9\u0022\u003ENHS England (the commissioning body for the English National Health Service) recently announced that from July 2017 all general practices in England will be contractually obliged to identify patients with moderate and severe frailty as part of the new General Medical Service contract. This is particularly challenging because frailty is a relatively new concept that does not have an agreed definition. Current approaches to defining frailty involve identifying patients with a collection of diagnoses, symptoms, and social factors.\u003Ca id=\u0022xref-ref-1-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-1\u0022\u003E1\u003C\/a\u003E\u003Ca id=\u0022xref-ref-2-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E These factors may be combined into a frailty score. This score is then used to identify patients at risk of important or preventable outcomes such as unplanned hospital admissions or death in the near future.\u003C\/p\u003E\u003Cp id=\u0022p-10\u0022\u003EAlthough recent guidance from the National Institute for Health Care and Excellence on multiple morbidities\u003Ca id=\u0022xref-ref-3-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-3\u0022\u003E3\u003C\/a\u003E has recommended tools to predict risk of unplanned hospital admissions,\u003Ca id=\u0022xref-ref-4-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-4\u0022\u003E4\u003C\/a\u003E\u003Ca id=\u0022xref-ref-5-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-5\u0022\u003E5\u003C\/a\u003E NICE was unable to identify any equations to reliably predict all cause mortality. NICE identified 41 studies that validated an equation to predict all cause mortality, all of which had major limitations. For example, some equations had been developed for purposes other than to predict all cause mortality.\u003Ca id=\u0022xref-ref-2-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E Other limitations were omitting key determinants of death, such as age and sex; giving equal weighting to all component factors within an equation (for example, wearing glasses could have equal weighting to ischaemic heart disease); using small unrepresentative samples; inappropriate handling of missing data; and poor reporting and poor performance of the tool in predicting death. The NICE guideline therefore recommended that research should be undertaken to develop new robust equations to identify patients with reduced life expectancy so that relevant assessments and interventions can be targeted appropriately.\u003C\/p\u003E\u003Cp id=\u0022p-11\u0022\u003EWe aimed to address the NICE research recommendation by developing a new equation to predict risk of death over a one year period among people aged 65 and older using a large validated medical research database of representative patients in primary care. Our secondary objective was to develop a definition of frailty directly based on risk of outcomes. Instead of creating a frailty index in the hope that it would predict unplanned admissions and all cause mortality, we decided to work the other way round. Starting with principled estimators of unplanned admissions and all cause mortality, we decided to develop a new classification of frailty, known as QFrailty. This would group people into four categories\u2014severely frail, moderately frail, mildly frail, or fit\u2014based on their absolute risks of an unplanned hospital admission or death within a year. This could then provide an outcomes based classification to improve on the electronic frailty index recommended by NHS England.\u003Ca id=\u0022xref-ref-2-3\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv class=\u0022section methods\u0022 id=\u0022sec-2\u0022\u003E\u003Ch2 class=\u0022\u0022\u003EMethods\u003C\/h2\u003E\u003Cdiv id=\u0022sec-3\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EStudy design and data source\u003C\/h3\u003E\u003Cp id=\u0022p-12\u0022\u003EWe undertook a cohort study in a large population of primary care patients in England who were registered with practices contributing to the QResearch database (version 42). All practices had to have used EMIS computer system for at least a year. We randomly allocated three quarters of the practices to the derivation dataset and the remainder to a validation dataset. We identified an open cohort of patients aged 65-100 years registered with practices between 1 January 2012 and 30 September 2016. Exclusions were patients who did not have a valid National Health Service number and those who did not have a postcode related Townsend score (eg, patients had moved to newly built houses with new postcodes not yet linked to deprivation data or patients were homeless or did not have a permanent residence). We determined an entry date to the cohort for each patient, which was the latest of his or her 65th birthday, date of registration with the practice plus one year, date on which the practice computer system was installed plus one year, and beginning of the study period (1 January 2012). Patients were censored at the earliest date of death, de-registration with the practice, last upload of computerised data, or the study end date (30 September 2016).\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-4\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EOutcomes\u003C\/h3\u003E\u003Cp id=\u0022p-13\u0022\u003EOur primary outcome was all cause mortality, using the date of death recorded on the QResearch database. We chose to evaluate risk of death at one year for comparability with other studies and to meet the requirements of the research recommendation in the NICE guidelines. The QResearch database is linked at individual patient level to the hospital admissions data and to mortality records obtained from the Office for National Statistics. The records are linked using a project specific pseudonymised NHS number. The recording of NHS numbers is valid and complete for 99.8% of QResearch patients, 99.9% for ONS mortality records, and 98% for hospital admissions records.\u003Ca id=\u0022xref-ref-4-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-4\u0022\u003E4\u003C\/a\u003E\u003Ca id=\u0022xref-ref-6-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-6\u0022\u003E6\u003C\/a\u003E\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-5\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EPredictor variables\u003C\/h3\u003E\u003Cp id=\u0022p-14\u0022\u003EWe examined several predictor variables (see box 1) based on established risk factors already included in the QAdmissions equation\u003Ca id=\u0022xref-ref-4-3\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-4\u0022\u003E4\u003C\/a\u003E (which predicts risk of unplanned hospital admissions) and variables highlighted in the related literature.\u003Ca id=\u0022xref-ref-2-4\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E\u003Ca id=\u0022xref-ref-3-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-3\u0022\u003E3\u003C\/a\u003E\u003Ca id=\u0022xref-ref-7-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-7\u0022\u003E7\u003C\/a\u003E\u003Ca id=\u0022xref-ref-8-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-8\u0022\u003E8\u003C\/a\u003E\u003Ca id=\u0022xref-ref-9-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-9\u0022\u003E9\u003C\/a\u003E\u003Ca id=\u0022xref-ref-10-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-10\u0022\u003E10\u003C\/a\u003E\u003C\/p\u003E\u003Cdiv class=\u0022boxed-text\u0022 id=\u0022boxed-text-1\u0022\u003E\u003Cdiv id=\u0022sec-6\u0022 class=\u0022subsection\u0022\u003E\u003Ch4\u003EBox 1 Predictor variables\u003C\/h4\u003E\u003Cul class=\u0022list-unord \u0022 id=\u0022list-1\u0022\u003E\u003Cli id=\u0022list-item-1\u0022\u003E\u003Cp id=\u0022p-15\u0022\u003EAge (continuous variable)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-2\u0022\u003E\u003Cp id=\u0022p-16\u0022\u003EGeographical region in England (10 regions)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-3\u0022\u003E\u003Cp id=\u0022p-17\u0022\u003ETownsend deprivation score. This is an area level continuous score based on the patients\u2019 postcode.\u003Ca id=\u0022xref-ref-11-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-11\u0022\u003E11\u003C\/a\u003E Originally developed by Townsend,\u003Ca id=\u0022xref-ref-11-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-11\u0022\u003E11\u003C\/a\u003E the score includes unemployment (as a percentage of those aged 16 or more who are economically active), non-car ownership (as a percentage of all households), non-home ownership (as a percentage of all households), and household overcrowding. These variables are measured for a given area of approximately 120 households, through the 2011 census, and combined to give a Townsend score for that area. A higher Townsend score implies a greater level of deprivation\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-4\u0022\u003E\u003Cp id=\u0022p-18\u0022\u003EEthnic group (nine categories)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-5\u0022\u003E\u003Cp id=\u0022p-19\u0022\u003EAlcohol intake (\u0026lt;1 unit\/day, 1-2 units\/day, 3-6 units\/day, 7-9 units\/day, \u22659 units\/day) (see \u003Ca href=\u0022http:\/\/www.nhs.uk\/Livewell\/alcohol\/Pages\/alcohol-units.aspx\u0022\u003Ewww.nhs.uk\/Livewell\/alcohol\/Pages\/alcohol-units.aspx\u003C\/a\u003E)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-6\u0022\u003E\u003Cp id=\u0022p-20\u0022\u003ESmoking status (non-smoker; former smoker; light, moderate, or heavy smoker)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-7\u0022\u003E\u003Cp id=\u0022p-21\u0022\u003EBody mass index (continuous variable)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-8\u0022\u003E\u003Cp id=\u0022p-22\u0022\u003EUnplanned admissions in past 12 months (0, 1, 2, or \u22653) as recorded on the linked hospital data\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-9\u0022\u003E\u003Cp id=\u0022p-23\u0022\u003EPoor mobility (poor mobility, housebound, confined to chair, bedridden, requires home visit, receives mobility allowance)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-10\u0022\u003E\u003Cp id=\u0022p-24\u0022\u003ELives in a care home (nursing home or residential care)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-11\u0022\u003E\u003Cp id=\u0022p-25\u0022\u003ELives alone\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-12\u0022\u003E\u003Cp id=\u0022p-26\u0022\u003EAtrial fibrillation\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-13\u0022\u003E\u003Cp id=\u0022p-27\u0022\u003ECongestive heart failure\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-14\u0022\u003E\u003Cp id=\u0022p-28\u0022\u003ECardiovascular disease (myocardial infarction, angina, stroke, or transient ischaemic attack)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-15\u0022\u003E\u003Cp id=\u0022p-29\u0022\u003EValvular heart disease\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-16\u0022\u003E\u003Cp id=\u0022p-30\u0022\u003EPeripheral vascular disease\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-17\u0022\u003E\u003Cp id=\u0022p-31\u0022\u003ETreated hypertension (hypertension and current antihypertensive treatment)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-18\u0022\u003E\u003Cp id=\u0022p-32\u0022\u003EChronic kidney disease (stages 4 or 5)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-19\u0022\u003E\u003Cp id=\u0022p-33\u0022\u003EDiabetes (none, type 1, type 2)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-20\u0022\u003E\u003Cp id=\u0022p-34\u0022\u003EHypothyroidism\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-21\u0022\u003E\u003Cp id=\u0022p-35\u0022\u003EHyperthyroidism\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-22\u0022\u003E\u003Cp id=\u0022p-36\u0022\u003ECancer\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-23\u0022\u003E\u003Cp id=\u0022p-37\u0022\u003EChronic liver disease or pancreatitis\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-24\u0022\u003E\u003Cp id=\u0022p-38\u0022\u003EMalabsorption (including Crohn\u2019s disease, ulcerative colitis, coeliac disease, steatorrhea, blind loop syndrome)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-25\u0022\u003E\u003Cp id=\u0022p-39\u0022\u003EPeptic ulcer (gastric or duodenal ulcer, simple or complicated ulcer)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-26\u0022\u003E\u003Cp id=\u0022p-40\u0022\u003EAsthma or chronic obstructive airways disease\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-27\u0022\u003E\u003Cp id=\u0022p-41\u0022\u003EEpilepsy\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-28\u0022\u003E\u003Cp id=\u0022p-42\u0022\u003EDementia\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-29\u0022\u003E\u003Cp id=\u0022p-43\u0022\u003ELearning disability\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-30\u0022\u003E\u003Cp id=\u0022p-44\u0022\u003EOsteoporosis\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-31\u0022\u003E\u003Cp id=\u0022p-45\u0022\u003EFragility fracture (hip, spine, shoulder, or wrist fracture)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-32\u0022\u003E\u003Cp id=\u0022p-46\u0022\u003EParkinson\u2019s disease or syndrome\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-33\u0022\u003E\u003Cp id=\u0022p-47\u0022\u003ERheumatoid arthritis\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-34\u0022\u003E\u003Cp id=\u0022p-48\u0022\u003EFalls\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-35\u0022\u003E\u003Cp id=\u0022p-49\u0022\u003EBipolar disorder or schizophrenia\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-36\u0022\u003E\u003Cp id=\u0022p-50\u0022\u003EDepression in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-37\u0022\u003E\u003Cp id=\u0022p-51\u0022\u003EVenous thromboembolism\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-38\u0022\u003E\u003Cp id=\u0022p-52\u0022\u003EAnaemia (haemoglobin \u0026lt;110 g\/L)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-39\u0022\u003E\u003Cp id=\u0022p-53\u0022\u003EAbnormal liver function test result (bilirubin, alanine aminotransferase, or \u03b3 glutamyltransferase more than three times the upper limit of normal)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-40\u0022\u003E\u003Cp id=\u0022p-54\u0022\u003EHigh platelet count (\u0026gt;480\u00d710\u003Csup\u003E9\u003C\/sup\u003E\/L)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-41\u0022\u003E\u003Cp id=\u0022p-55\u0022\u003ELeg ulcer (leg, shin, ankle or foot ulcer, ischaemic neuropathic, arterial, or venous ulcer)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-42\u0022\u003E\u003Cp id=\u0022p-56\u0022\u003EBlindness (registered blind or partially sighted or visual impairment)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-43\u0022\u003E\u003Cp id=\u0022p-57\u0022\u003EAppetite loss in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-44\u0022\u003E\u003Cp id=\u0022p-58\u0022\u003EWeight loss in past 12 months (unexplained or abnormal weight loss)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-45\u0022\u003E\u003Cp id=\u0022p-59\u0022\u003EUrinary incontinence in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-46\u0022\u003E\u003Cp id=\u0022p-60\u0022\u003ENocturia in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-47\u0022\u003E\u003Cp id=\u0022p-61\u0022\u003EUrinary retention in past 12 months (acute or chronic retention)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-48\u0022\u003E\u003Cp id=\u0022p-62\u0022\u003ESyncope (vasovagal symptom, faint, collapse, \u201cfunny turn,\u201d drop attack) in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-49\u0022\u003E\u003Cp id=\u0022p-63\u0022\u003EDizziness in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-50\u0022\u003E\u003Cp id=\u0022p-64\u0022\u003EInsomnia in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-51\u0022\u003E\u003Cp id=\u0022p-65\u0022\u003EDyspnoea in past 12 months (breathless at rest or on exertion, paroxysmal nocturnal dyspnoea)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-52\u0022\u003E\u003Cp id=\u0022p-66\u0022\u003EHearing impairment or deafness in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-53\u0022\u003E\u003Cp id=\u0022p-67\u0022\u003ELoneliness in past 12 months\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-54\u0022\u003E\u003Cp id=\u0022p-68\u0022\u003EUse of anticoagulants (\u22652 prescriptions in past six months)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-55\u0022\u003E\u003Cp id=\u0022p-69\u0022\u003EUse of antidepressants (\u22652 prescriptions in past six months)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-56\u0022\u003E\u003Cp id=\u0022p-70\u0022\u003EUse of antipsychotics (\u22652 prescriptions in past six months)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-57\u0022\u003E\u003Cp id=\u0022p-71\u0022\u003EUse of corticosteroids (\u22652 prescriptions in past six months)\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-58\u0022\u003E\u003Cp id=\u0022p-72\u0022\u003ENon-steroidal anti-inflammatory drugs (\u22652 prescriptions in past six months)\u003C\/p\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp id=\u0022p-73\u0022\u003EThe number of unplanned hospital admissions in the previous 12 months was derived from information on the linked hospital records. All predictor variables were based on the latest coded information recorded in the general practice record before entry to the cohort.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-7\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EDerivation and validation of the models\u003C\/h3\u003E\u003Cp id=\u0022p-74\u0022\u003EWe developed and validated the risk prediction equations using established methods.\u003Ca id=\u0022xref-ref-10-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-10\u0022\u003E10\u003C\/a\u003E\u003Ca id=\u0022xref-ref-12-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-12\u0022\u003E12\u003C\/a\u003E\u003Ca id=\u0022xref-ref-13-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-13\u0022\u003E13\u003C\/a\u003E\u003Ca id=\u0022xref-ref-14-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-14\u0022\u003E14\u003C\/a\u003E\u003Ca id=\u0022xref-ref-15-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-15\u0022\u003E15\u003C\/a\u003E To replace missing values for body mass index, smoking status, and alcohol intake we used multiple imputation with chained equations and used these values in our main analyses.\u003Ca id=\u0022xref-ref-16-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-16\u0022\u003E16\u003C\/a\u003E\u003Ca id=\u0022xref-ref-17-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-17\u0022\u003E17\u003C\/a\u003E\u003Ca id=\u0022xref-ref-18-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-18\u0022\u003E18\u003C\/a\u003E\u003Ca id=\u0022xref-ref-19-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-19\u0022\u003E19\u003C\/a\u003E We carried out five imputations as this has relatively high efficiency\u003Ca id=\u0022xref-ref-20-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-20\u0022\u003E20\u003C\/a\u003E and was a pragmatic approach accounting for the size of the datasets and capacity of the available servers and software. We included all predictor variables in the imputation model, along with age interaction terms, the Nelson-Aalen estimator of the baseline cumulative hazard, and the outcome indicator.\u003C\/p\u003E\u003Cp id=\u0022p-75\u0022\u003ECox\u2019s proportional hazards models estimated the coefficients for each risk factor in men and women separately. We used Rubin\u2019s rules to combine the results across the imputed datasets.\u003Ca id=\u0022xref-ref-21-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-21\u0022\u003E21\u003C\/a\u003E We used fractional polynomials\u003Ca id=\u0022xref-ref-22-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-22\u0022\u003E22\u003C\/a\u003E to model non-linear risk relations with continuous variables (age and body mass index) using data from patients with recorded values to derive the fractional polynomial terms. Initially we fitted full models. We retained variables if they had an adjusted hazard ratio of \u0026lt;0.90 or \u0026gt;1.10 (for binary variables) and were statistically significant at the 0.01 level. We examined interactions between predictor variables and age at study entry and included statistically significant interactions in the final models.\u003C\/p\u003E\u003Cp id=\u0022p-76\u0022\u003EFor each variable from the final model we used the regression coefficients as weights, which we combined with the baseline survivor function at one year to derive risk equations.\u003Ca id=\u0022xref-ref-23-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-23\u0022\u003E23\u003C\/a\u003E We estimated the baseline survivor function based on zero values of centred continuous variables, with all binary predictor values set to zero.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-8\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EValidation of the models\u003C\/h3\u003E\u003Cp id=\u0022p-77\u0022\u003EIn the validation cohort we used multiple imputation to replace missing values for body mass index, smoking status, and alcohol intake. Five imputations were done. We applied the risk equations for men and women obtained from the derivation cohort to the validation cohort and calculated measures of discrimination. As in previous studies,\u003Ca id=\u0022xref-ref-24-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-24\u0022\u003E24\u003C\/a\u003E we calculated R\u003Csup\u003E2\u003C\/sup\u003E values (explained variation where higher values indicate a greater proportion of variation in survival time explained by the model\u003Ca id=\u0022xref-ref-25-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-25\u0022\u003E25\u003C\/a\u003E), D statistic\u003Ca id=\u0022xref-ref-26-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-26\u0022\u003E26\u003C\/a\u003E (a measure of discrimination that quantifies the separation in survival between patients with different levels of predicted risk, where higher values indicate better discrimination), and Harrell\u2019s C statistic at one year and combined these across datasets using Rubin\u2019s rules. Harrell\u2019s C statistic\u003Ca id=\u0022xref-ref-27-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-27\u0022\u003E27\u003C\/a\u003E is a measure of discrimination (separation) that quantifies the extent to which those with earlier events have higher risk scores. It is similar to the receiver operating characteristic statistic but takes account of the censored nature of the data. Higher values of Harrell\u2019s C indicate better performance of the model for predicting the relevant outcome. A value of 1 indicates the model has perfect discrimination. A value of 0.5 indicates that the model discrimination is no better than chance. We also evaluated these performance measures in five age groups and nine ethnic groups. We calculated 95% confidence intervals for the performance statistics to allow comparisons with alternative models for the same outcome and across different subgroups.\u003Ca id=\u0022xref-ref-28-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-28\u0022\u003E28\u003C\/a\u003E\u003C\/p\u003E\u003Cp id=\u0022p-78\u0022\u003EWe assessed calibration of the mortality score by comparing the mean predicted risks evaluated at one year with the observed risks by 10th of predicted risk. The observed risks were obtained using the Kaplan-Meier estimates evaluated at one year for men and women. We also evaluated performance by calculating Harrell\u2019s C statistics in individual general practices and combined the results using meta-analytical techniques.\u003Ca id=\u0022xref-ref-29-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-29\u0022\u003E29\u003C\/a\u003E\u003C\/p\u003E\u003Cp id=\u0022p-79\u0022\u003EWe also applied the latest version of the QAdmissions score to the validation cohort and calculated measures of discrimination for unplanned hospital admissions over one year.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-9\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EDecision curve analysis\u003C\/h3\u003E\u003Cp id=\u0022p-80\u0022\u003EWe used decision curve analysis in the validation cohort to evaluate the net benefits of the mortality score.\u003Ca id=\u0022xref-ref-30-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-30\u0022\u003E30\u003C\/a\u003E\u003Ca id=\u0022xref-ref-31-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-31\u0022\u003E31\u003C\/a\u003E\u003Ca id=\u0022xref-ref-32-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-32\u0022\u003E32\u003C\/a\u003E This method assesses the benefits of correctly identifying people who will have an event compared with the harms from a false positive classification (which could, for example, lead to unnecessary distress or interventions). The net benefit of a risk equation at a given risk threshold is given by calculating the difference between the proportion of true positives and the proportion of false positives multiplied by the odds of the risk threshold. We calculated the net benefits across a range of threshold probabilities and compared these with alternative strategies of \u201cintervention in everyone\u201d and \u201cintervention in no one.\u201d In general, the strategy with the highest net benefit at any given risk threshold is considered to have the most clinical value.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-10\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EDevelopment of frailty categories\u003C\/h3\u003E\u003Cp id=\u0022p-81\u0022\u003ESince there is no currently accepted threshold for classifying high risk of death, we examined the distribution of predicted risks and calculated a series of centile values. For each centile threshold, we calculated the sensitivity, specificity, and positive and negative predictive values of death over a one year follow-up period. Using the latest version of the QAdmissions score we also examined the distribution of patients by their risk of unplanned hospital admission over one year.\u003Ca id=\u0022xref-ref-4-4\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-4\u0022\u003E4\u003C\/a\u003E We identified unplanned admissions using the hospital episode statistics linked to QResearch as in the original paper.\u003Ca id=\u0022xref-ref-4-5\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-4\u0022\u003E4\u003C\/a\u003E We then classified patients into four frailty groups based on a combination of their predicted risk of unplanned admission and their predicted risk of death over the next 12 months such that the proportion of patients in each group was broadly similar to that published elsewhere for the \u201celectronic frailty score\u201d (EFI) based on a similar English population.\u003Ca id=\u0022xref-ref-2-5\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E In the internal validation of the EFI score, 3% of the validation cohort were categorised as having severe frailty, 12% as having moderate frailty, 35% as having mild frailty, and 50% as fit. The corresponding values for the EFI external validation cohort were 4%, 16%, 37%, and 43%.\u003C\/p\u003E\u003Cp id=\u0022p-82\u0022\u003EWe repeated some analyses, restricting the validation cohort to those with two or more medical conditions who would meet the NICE broad definition of having multiple morbidities. The supplementary tables present the results. To maximise the power and also generalisability of the results we used all the relevant patients on the database. STATA (version 14) was used for all analyses. We adhered to the TRIPOD statement for reporting.\u003Ca id=\u0022xref-ref-33-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-33\u0022\u003E33\u003C\/a\u003E\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-11\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EPatient involvement\u003C\/h3\u003E\u003Cp id=\u0022p-83\u0022\u003ENo patients were involved in setting the research question or the outcome measures, nor were they involved in the design or implementation of the study. Patient representatives from the QResearch advisory board have written the information for patients on the QResearch website about the use of the database for research. They have also advised on dissemination of the results, including the use of lay summaries describing the research and its results.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022section results\u0022 id=\u0022sec-12\u0022\u003E\u003Ch2 class=\u0022\u0022\u003EResults\u003C\/h2\u003E\u003Cdiv id=\u0022sec-13\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EOverall study population\u003C\/h3\u003E\u003Cp id=\u0022p-84\u0022\u003E\u003Cem\u003EDerivation cohort\u003C\/em\u003E\u2014overall, 1436 QResearch practices in England met our inclusion criteria, of which 1079 were randomly assigned to the derivation dataset, with the remainder (n=357) assigned to the validation cohort. We identified 1\u2009471\u2009558 patients in the derivation cohort aged 65-100 years. Of these, we excluded 2550 (0.2%) who did not have a valid NHS number and a further 2410 (0.2%) who did not have a recorded Townsend score, leaving 1\u2009466\u2009598 for the derivation analysis.\u003C\/p\u003E\u003Cp id=\u0022p-85\u0022\u003E\u003Cem\u003EValidation cohort\u003C\/em\u003E\u2014we identified 500\u2009816 patients in the validation cohort aged 65-100 years. Of those, we excluded 505 (0.1%) who did not have a valid NHS number and a further 833 (0.2%) who did not have a recorded Townsend score, leaving 499\u2009478 for the validation analysis.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-14\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EBaseline characteristics\u003C\/h3\u003E\u003Cp id=\u0022p-86\u0022\u003ETable 1\u003Ca id=\u0022xref-table-wrap-1-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T1\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows the baseline characteristics of men and women in the derivation and validation cohorts. In the derivation cohort, self assigned ethnicity was recorded in 80.3% (n=1\u2009177\u2009596), smoking status in 99.0% (n=1\u2009451\u2009343), alcohol intake in 92.0% (n=1\u2009349\u2009728), and body mass index in 90.2% (n=1\u2009322\u2009929). Overall, 86.8% (n=1 273 310) had complete data for smoking status, alcohol intake, and body mass index. The mean age was 75.3 years, and 12.0% (n=175\u2009915) of patients had one or more unplanned hospital admissions in the past 12 months, 42.8% (n=628\u2009106) had treated hypertension, 21.0% (n=307\u2009499) had cardiovascular disease, 15.1% (n=220\u2009886) had type 2 diabetes, 18.8% (n=276\u2009001) were prescribed antidepressants, and 18.3% (n=268\u2009821) were prescribed non-steroidal anti-inflammatory drugs (NSAIDs). The corresponding results for the validation cohort were similar.\u003C\/p\u003E\u003Cdiv id=\u0022T1\u0022 class=\u0022table pos-float\u0022\u003E\u003Cdiv class=\u0022table\u0022\u003E\u003Cdiv class=\u0022table-caption\u0022\u003E\u003Cspan class=\u0022table-label\u0022\u003ETable 1\u003C\/span\u003E \u003Cp id=\u0022p-87\u0022 class=\u0022first-child\u0022\u003EBaseline characteristics of patients aged 65-100 years in derivation and validation cohorts. Values are numbers (percentages) unless stated otherwise\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022table-inline\u0022\u003E\u003Cdiv class=\u0022callout\u0022\u003E\u003Cspan\u003EView this table:\u003C\/span\u003E\u003Cul class=\u0022callout-links\u0022\u003E\u003Cli\u003E\u003Ca href=\u0022\/highwire\/markup\/951351\/expansion?width=1000\u0026amp;height=500\u0026amp;iframe=true\u0026amp;postprocessors=highwire_figures%2Chighwire_math\u0022 class=\u0022colorbox colorbox-load table-expand-popup\u0022 rel=\u0022gallery-fragment-tables\u0022\u003EView popup\u003C\/a\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca href=\u0022##\u0022 class=\u0022table-expand-inline\u0022 data-table-url=\u0022\/highwire\/markup\/951351\/expansion?postprocessors=highwire_figures%2Chighwire_math\u0022\u003EView inline\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp id=\u0022p-92\u0022\u003ESupplementary table 1 shows the patients\u2019 number of medical conditions. In the derivation cohort, 17.3% (n=253\u2009585) did not have any of the 29 conditions listed, 23.9% (n=350\u2009994) had one condition, and 58.8% (n=862\u2009019) had two or more conditions.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-15\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EIncidence of death\u003C\/h3\u003E\u003Cp id=\u0022p-93\u0022\u003ETable 2\u003Ca id=\u0022xref-table-wrap-2-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T2\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows the number of patients who died during the study period, the person years of follow-up, and the death rates by age and sex. Overall in the derivation cohort, 180\u2009132 deaths arose from 4.39 million person years of follow-up. In the validation cohort, 61\u2009446 deaths arose from 1.49 million person years of follow-up. In the derivation and validation cohorts 581\u2009702 and 197\u2009834 people, respectively, had five or more years of follow-up.\u003C\/p\u003E\u003Cdiv id=\u0022T2\u0022 class=\u0022table pos-float\u0022\u003E\u003Cdiv class=\u0022table\u0022\u003E\u003Cdiv class=\u0022table-caption\u0022\u003E\u003Cspan class=\u0022table-label\u0022\u003ETable 2\u003C\/span\u003E \u003Cp id=\u0022p-94\u0022 class=\u0022first-child\u0022\u003ENumber of deaths, person years of follow up, and death rate per 1000 person years of observation (95% confidence intervals) in derivation and validation cohort\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022table-inline\u0022\u003E\u003Cdiv class=\u0022callout\u0022\u003E\u003Cspan\u003EView this table:\u003C\/span\u003E\u003Cul class=\u0022callout-links\u0022\u003E\u003Cli\u003E\u003Ca href=\u0022\/highwire\/markup\/951355\/expansion?width=1000\u0026amp;height=500\u0026amp;iframe=true\u0026amp;postprocessors=highwire_figures%2Chighwire_math\u0022 class=\u0022colorbox colorbox-load table-expand-popup\u0022 rel=\u0022gallery-fragment-tables\u0022\u003EView popup\u003C\/a\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca href=\u0022##\u0022 class=\u0022table-expand-inline\u0022 data-table-url=\u0022\/highwire\/markup\/951355\/expansion?postprocessors=highwire_figures%2Chighwire_math\u0022\u003EView inline\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-16\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EPredictor variables\u003C\/h3\u003E\u003Cp id=\u0022p-95\u0022\u003ETable 3\u003Ca id=\u0022xref-table-wrap-3-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T3\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows the adjusted hazard ratios for the final models for women and men in the derivation cohort. The final model included the variables: fractional polynomial terms for age, fractional polynomial terms for body mass index, Townsend score, ethnic group, smoking status, alcohol intake, unplanned hospital admissions in the past 12 months, atrial fibrillation, antipsychotics, cancer, asthma or chronic obstructive pulmonary disease, living in a care home, congestive heart failure, corticosteroids, cardiovascular disease, dementia, epilepsy, learning disability, leg ulcer, chronic liver disease or pancreatitis, Parkinson\u2019s disease, poor mobility, rheumatoid arthritis, chronic kidney disease, type 1 diabetes, type 2 diabetes, venous thromboembolism, anaemia, abnormal liver function test result, high platelet count, and visits to a general practitioner in the past 12 months with either appetite loss, unexplained weight loss, or dyspnoea (breathlessness). The other variables tested did not meet the criteria for inclusion in the final model.\u003C\/p\u003E\u003Cdiv id=\u0022T3\u0022 class=\u0022table pos-float\u0022\u003E\u003Cdiv class=\u0022table\u0022\u003E\u003Cdiv class=\u0022table-caption\u0022\u003E\u003Cspan class=\u0022table-label\u0022\u003ETable 3\u003C\/span\u003E \u003Cp id=\u0022p-96\u0022 class=\u0022first-child\u0022\u003EAdjusted hazard ratios (95% confidence interval) for death in derivation cohort. Models also include fractional polynomial terms for age and body mass index\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022table-inline\u0022\u003E\u003Cdiv class=\u0022callout\u0022\u003E\u003Cspan\u003EView this table:\u003C\/span\u003E\u003Cul class=\u0022callout-links\u0022\u003E\u003Cli\u003E\u003Ca href=\u0022\/highwire\/markup\/951356\/expansion?width=1000\u0026amp;height=500\u0026amp;iframe=true\u0026amp;postprocessors=highwire_figures%2Chighwire_math\u0022 class=\u0022colorbox colorbox-load table-expand-popup\u0022 rel=\u0022gallery-fragment-tables\u0022\u003EView popup\u003C\/a\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca href=\u0022##\u0022 class=\u0022table-expand-inline\u0022 data-table-url=\u0022\/highwire\/markup\/951356\/expansion?postprocessors=highwire_figures%2Chighwire_math\u0022\u003EView inline\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp id=\u0022p-102\u0022\u003EThe graphs in figures 1 and 2\u003Ca id=\u0022xref-fig-1-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#F1 F2\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E show the adjusted hazard ratios in women and men, respectively, for age interaction terms that were statistically significant (see footnote in table 3\u003Ca id=\u0022xref-table-wrap-3-2\u0022 class=\u0022xref-up-link\u0022 href=\u0022#T3\u0022\u003E\u003Cspan\u003E\u21d1\u003C\/span\u003E\u003C\/a\u003E). For each of these interactions, hazard ratios for the predictors were higher at younger ages compared with older ages.\u003C\/p\u003E\u003Cdiv id=\u0022F1\u0022 class=\u0022fig pos-float type-figure odd\u0022\u003E\u003Cdiv class=\u0022fig-inline\u0022\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F1.large.jpg?width=800\u0026amp;height=600\u0022 title=\u0022Fig 1\u0026#xA0;Hazard ratios for all cause mortality in women by age\u0022 class=\u0022colorbox fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;\u0026lt;div class=\u0026quot;fig-caption\u0026quot; xmlns:xhtml=\u0026quot;http:\/\/www.w3.org\/1999\/xhtml\u0026quot;\u0026gt;\u0026lt;p id=\u0026quot;p-103\u0026quot; class=\u0026quot;first-child\u0026quot;\u0026gt;\u0026lt;strong\u0026gt;Fig 1\u0026lt;\/strong\u0026gt;\u0026#xA0;Hazard ratios for all cause mortality in women by age\u0026lt;\/p\u0026gt;\u0026lt;div class=\u0026quot;sb-div caption-clear\u0026quot;\u0026gt;\u0026lt;\/div\u0026gt;\u0026lt;\/div\u0026gt;\u0026lt;\/div\u0026gt;\u0022\u003E\u003Cimg class=\u0022fragment-image\u0022 src=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F1.medium.gif\u0022 alt=\u0022Figure1\u0022\/\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 class=\u0022fig-caption\u0022\u003E\u003Cp id=\u0022p-103\u0022 class=\u0022first-child\u0022\u003E\u003Cstrong\u003EFig 1\u003C\/strong\u003E\u00a0Hazard ratios for all cause mortality in women by age\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links list-inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F1.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure1\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F1.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/951353\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv id=\u0022F2\u0022 class=\u0022fig pos-float type-figure odd\u0022\u003E\u003Cdiv class=\u0022fig-inline\u0022\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F2.large.jpg?width=800\u0026amp;height=600\u0022 title=\u0022Fig 2\u0026#xA0;Hazard ratios for all cause mortality in men by age\u0022 class=\u0022colorbox fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;\u0026lt;div class=\u0026quot;fig-caption\u0026quot; xmlns:xhtml=\u0026quot;http:\/\/www.w3.org\/1999\/xhtml\u0026quot;\u0026gt;\u0026lt;\/div\u0026gt;\u0026lt;\/div\u0026gt;\u0022\u003E\u003Cimg class=\u0022fragment-image\u0022 src=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F2.medium.gif\u0022 alt=\u0022Figure2\u0022\/\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 class=\u0022fig-caption\u0022\u003E\u003Cp id=\u0022p-104\u0022 class=\u0022first-child\u0022\u003E\u003Cstrong\u003EFig 2\u003C\/strong\u003E\u00a0Hazard ratios for all cause mortality in men by age\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links list-inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F2.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure2\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F2.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/951358\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-17\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EValidation\u003C\/h3\u003E\u003Cdiv id=\u0022sec-18\u0022 class=\u0022subsection\u0022\u003E\u003Ch4\u003EDiscrimination\u003C\/h4\u003E\u003Cp id=\u0022p-105\u0022\u003ETable 4\u003Ca id=\u0022xref-table-wrap-4-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T4\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows the performance of the QMortality score in the validation cohort for women and men at one year. Overall, the values for the R\u003Csup\u003E2\u003C\/sup\u003E, D, and C statistics were higher in women than in men indicating that the score performed better in women than in men. The mortality equation in women explained 55.6% of the variation in time to death (R\u003Csup\u003E2\u003C\/sup\u003E), the D statistic was 2.29, and Harrell\u2019s C statistic was 0.85. The corresponding values for men were 53.1%, 2.18, and 0.84. Table 4\u003Ca id=\u0022xref-table-wrap-4-2\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T4\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E also shows results for the latest version of the QAdmissions equation for predicting unplanned admissions (based on 160\u2009217 unplanned admissions in the validation cohort over a one year period). Table 4\u003Ca id=\u0022xref-table-wrap-4-3\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T4\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows that the performance of the QMortality score for predicting deaths was better than the performance of the QAdmissions score for predicting unplanned admissions.\u003C\/p\u003E\u003Cdiv id=\u0022T4\u0022 class=\u0022table pos-float\u0022\u003E\u003Cdiv class=\u0022table\u0022\u003E\u003Cdiv class=\u0022table-caption\u0022\u003E\u003Cspan class=\u0022table-label\u0022\u003ETable 4\u003C\/span\u003E \u003Cp id=\u0022p-106\u0022 class=\u0022first-child\u0022\u003EPerformance of QMortality algorithm to predict one year risk of death, and QAdmissions score to predict risk of unplanned admission over one year in men and women aged 65-100 years in validation cohort\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022table-inline\u0022\u003E\u003Cdiv class=\u0022callout\u0022\u003E\u003Cspan\u003EView this table:\u003C\/span\u003E\u003Cul class=\u0022callout-links\u0022\u003E\u003Cli\u003E\u003Ca href=\u0022\/highwire\/markup\/951348\/expansion?width=1000\u0026amp;height=500\u0026amp;iframe=true\u0026amp;postprocessors=highwire_figures%2Chighwire_math\u0022 class=\u0022colorbox colorbox-load table-expand-popup\u0022 rel=\u0022gallery-fragment-tables\u0022\u003EView popup\u003C\/a\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca href=\u0022##\u0022 class=\u0022table-expand-inline\u0022 data-table-url=\u0022\/highwire\/markup\/951348\/expansion?postprocessors=highwire_figures%2Chighwire_math\u0022\u003EView inline\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp id=\u0022p-107\u0022\u003ESupplementary table 2 shows the results for the mortality scores by age group and ethnic group. Performance tended to be better in the younger age groups but was similar across all ethnic groups.\u003C\/p\u003E\u003Cp id=\u0022p-108\u0022\u003EFigure 3\u003Ca id=\u0022xref-fig-3-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#F3\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows plots of Harrell\u2019s C statistic for each general practice in the validation cohort against the number of deaths in each practice in women and men separately. The summary (average) C statistic for women was 0.854 (95% confidence interval 0.850 to 0.859) from a random effects meta-analysis. The I\u003Csup\u003E2\u003C\/sup\u003E value (ie, the percentage of total variation in C statistic owing to heterogeneity between practices) was 63.2%. The approximate 95% prediction interval for the true C statistic in women in a new practice was 0.80 to 0.91. For men, the summary C statistic was 0.844 (95% confidence interval 0.839 to 0.849). The I\u003Csup\u003E2\u003C\/sup\u003E value was 70.3%. The approximate 95% prediction interval for the true C statistic in men in a new practice was 0.76 to 0.92.\u003C\/p\u003E\u003Cdiv id=\u0022F3\u0022 class=\u0022fig pos-float type-figure odd\u0022\u003E\u003Cdiv class=\u0022fig-inline\u0022\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F3.large.jpg?width=800\u0026amp;height=600\u0022 title=\u0022Fig 3\u0026#xA0;Plots of discrimination across 357 general practices showing Harrell\u0026#x2019;s C statistic against number of deaths in women and men\u0022 class=\u0022colorbox fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;\u0026lt;div class=\u0026quot;fig-caption\u0026quot; xmlns:xhtml=\u0026quot;http:\/\/www.w3.org\/1999\/xhtml\u0026quot;\u0026gt;\u0026lt;\/div\u0026gt;\u0026lt;\/div\u0026gt;\u0022\u003E\u003Cimg class=\u0022fragment-image\u0022 src=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F3.medium.gif\u0022 alt=\u0022Figure3\u0022\/\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 class=\u0022fig-caption\u0022\u003E\u003Cp id=\u0022p-109\u0022 class=\u0022first-child\u0022\u003E\u003Cstrong\u003EFig 3\u003C\/strong\u003E\u00a0Plots of discrimination across 357 general practices showing Harrell\u2019s C statistic against number of deaths in women and men\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links list-inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F3.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure3\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F3.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/951359\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp id=\u0022p-110\u0022\u003ESupplementary table 3 shows the validation statistics for the mortality score among patients with two or more morbidities (as required by the NICE guideline\u003Ca id=\u0022xref-ref-3-5\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-3\u0022\u003E3\u003C\/a\u003E).\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-19\u0022 class=\u0022subsection\u0022\u003E\u003Ch4\u003ECalibration\u003C\/h4\u003E\u003Cp id=\u0022p-111\u0022\u003EFigure 4\u003Ca id=\u0022xref-fig-4-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#F4\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E displays the observed risks and mean predicted risks of death across each 10th of the predicted risk score (1 representing the lowest risk and 10 the highest risk). This shows that the equation was well calibrated. Supplementary figure 1a-e shows the calibration within each age group. Supplementary table 5 shows overall calibration by age group and ethnic group and for the top 2%, 10%, and 50% of predicted risk. The results were generally good except for over-prediction in Chinese women and under-prediction in black African women, although numbers of deaths were relatively small in these subgroups.\u003C\/p\u003E\u003Cdiv id=\u0022F4\u0022 class=\u0022fig pos-float type-figure odd\u0022\u003E\u003Cdiv class=\u0022fig-inline\u0022\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F4.large.jpg?width=800\u0026amp;height=600\u0022 title=\u0022Fig 4\u0026#xA0;Predicted and observed risk of all cause mortality at one year in women and men\u0022 class=\u0022colorbox fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;\u0026lt;div class=\u0026quot;fig-caption\u0026quot; xmlns:xhtml=\u0026quot;http:\/\/www.w3.org\/1999\/xhtml\u0026quot;\u0026gt;\u0026lt;\/div\u0026gt;\u0026lt;\/div\u0026gt;\u0022\u003E\u003Cimg class=\u0022fragment-image\u0022 src=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F4.medium.gif\u0022 alt=\u0022Figure4\u0022\/\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 class=\u0022fig-caption\u0022\u003E\u003Cp id=\u0022p-112\u0022 class=\u0022first-child\u0022\u003E\u003Cstrong\u003EFig 4\u003C\/strong\u003E\u00a0Predicted and observed risk of all cause mortality at one year in women and men\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links list-inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F4.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure4\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F4.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/951357\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-20\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EDecision curve analysis\u003C\/h3\u003E\u003Cp id=\u0022p-113\u0022\u003EFigure 5\u003Ca id=\u0022xref-fig-5-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#F5\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E displays the net benefit curves for the mortality equations at one year in men and women. These show that the prediction equations had higher net benefit than did strategies based on considering either no patients or all patients for intervention for risk thresholds up to around 50%.\u003C\/p\u003E\u003Cdiv id=\u0022F5\u0022 class=\u0022fig pos-float type-figure odd\u0022\u003E\u003Cdiv class=\u0022fig-inline\u0022\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F5.large.jpg?width=800\u0026amp;height=600\u0022 title=\u0022Fig 5\u0026#xA0;Decision curve analysis for women and men\u0022 class=\u0022colorbox fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;\u0026lt;div class=\u0026quot;fig-caption\u0026quot; xmlns:xhtml=\u0026quot;http:\/\/www.w3.org\/1999\/xhtml\u0026quot;\u0026gt;\u0026lt;\/div\u0026gt;\u0026lt;\/div\u0026gt;\u0022\u003E\u003Cimg class=\u0022fragment-image\u0022 src=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F5.medium.gif\u0022 alt=\u0022Figure5\u0022\/\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 class=\u0022fig-caption\u0022\u003E\u003Cp id=\u0022p-114\u0022 class=\u0022first-child\u0022\u003E\u003Cstrong\u003EFig 5\u003C\/strong\u003E\u00a0Decision curve analysis for women and men\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links list-inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F5.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure5\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/bmj\/358\/bmj.j4208\/F5.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\n\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/951354\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-21\u0022 class=\u0022subsection\u0022\u003E\u003Ch4\u003ESensitivity and specificity\u003C\/h4\u003E\u003Cp id=\u0022p-115\u0022\u003ETable 5\u003Ca id=\u0022xref-table-wrap-5-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T5\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows the sensitivity, specificity, and positive and negative predictive values for the mortality equation at one year for various thresholds based on patients in the validation cohort.\u003C\/p\u003E\u003Cdiv id=\u0022T5\u0022 class=\u0022table pos-float\u0022\u003E\u003Cdiv class=\u0022table\u0022\u003E\u003Cdiv class=\u0022table-caption\u0022\u003E\u003Cspan class=\u0022table-label\u0022\u003ETable 5\u003C\/span\u003E \u003Cp id=\u0022p-116\u0022 class=\u0022first-child\u0022\u003ESensitivity, specificity, and positive and negative predictive values for death at different thresholds of predicted risk of death over one year in validation cohort\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022table-inline\u0022\u003E\u003Cdiv class=\u0022callout\u0022\u003E\u003Cspan\u003EView this table:\u003C\/span\u003E\u003Cul class=\u0022callout-links\u0022\u003E\u003Cli\u003E\u003Ca href=\u0022\/highwire\/markup\/951352\/expansion?width=1000\u0026amp;height=500\u0026amp;iframe=true\u0026amp;postprocessors=highwire_figures%2Chighwire_math\u0022 class=\u0022colorbox colorbox-load table-expand-popup\u0022 rel=\u0022gallery-fragment-tables\u0022\u003EView popup\u003C\/a\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca href=\u0022##\u0022 class=\u0022table-expand-inline\u0022 data-table-url=\u0022\/highwire\/markup\/951352\/expansion?postprocessors=highwire_figures%2Chighwire_math\u0022\u003EView inline\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp id=\u0022p-117\u0022\u003ETable 6\u003Ca id=\u0022xref-table-wrap-6-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T6\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows that the risk threshold for the top 2% at highest risk of death in the next year was 47.0%, for the top 10% was 20.3%, and for the top 50% was 2.9%. With a risk threshold of 20.3% over one year to identify the 10% of patients with the highest risk of death, the sensitivity for identifying deaths was 37.4%, specificity 97.3%, positive predictive value 46.0%, and negative predictive value 91.4%. Supplementary table 4 shows the results restricted to patients with two or more medical conditions.\u003C\/p\u003E\u003Cdiv id=\u0022T6\u0022 class=\u0022table pos-float\u0022\u003E\u003Cdiv class=\u0022table\u0022\u003E\u003Cdiv class=\u0022table-caption\u0022\u003E\u003Cspan class=\u0022table-label\u0022\u003ETable 6\u003C\/span\u003E \u003Cp id=\u0022p-118\u0022 class=\u0022first-child\u0022\u003EThresholds for classification of patients into one of four frailty groups\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022table-inline\u0022\u003E\u003Cdiv class=\u0022callout\u0022\u003E\u003Cspan\u003EView this table:\u003C\/span\u003E\u003Cul class=\u0022callout-links\u0022\u003E\u003Cli\u003E\u003Ca href=\u0022\/highwire\/markup\/951349\/expansion?width=1000\u0026amp;height=500\u0026amp;iframe=true\u0026amp;postprocessors=highwire_figures%2Chighwire_math\u0022 class=\u0022colorbox colorbox-load table-expand-popup\u0022 rel=\u0022gallery-fragment-tables\u0022\u003EView popup\u003C\/a\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca href=\u0022##\u0022 class=\u0022table-expand-inline\u0022 data-table-url=\u0022\/highwire\/markup\/951349\/expansion?postprocessors=highwire_figures%2Chighwire_math\u0022\u003EView inline\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp id=\u0022p-121\u0022\u003EThe corresponding thresholds for risk of unplanned hospital admission over one year were 60.7% to identify the top 2%, 34.0% for the top 10%, and 10.0% for the top 50% (results not shown).\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-22\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EClassification of frailty\u003C\/h3\u003E\u003Cp id=\u0022p-122\u0022\u003ETable 7\u003Ca id=\u0022xref-table-wrap-7-1\u0022 class=\u0022xref-down-link\u0022 href=\u0022#T7\u0022\u003E\u003Cspan\u003E\u21d3\u003C\/span\u003E\u003C\/a\u003E shows the characteristics of patients from the validation cohort split into four QFrailty groups that are broadly equivalent to the proportion of patients reported to be in the four categories according to the EFI.\u003Ca id=\u0022xref-ref-2-6\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E\u003C\/p\u003E\u003Cdiv id=\u0022T7\u0022 class=\u0022table pos-float\u0022\u003E\u003Cdiv class=\u0022table\u0022\u003E\u003Cdiv class=\u0022table-caption\u0022\u003E\u003Cspan class=\u0022table-label\u0022\u003ETable 7\u003C\/span\u003E \u003Cp id=\u0022p-123\u0022 class=\u0022first-child\u0022\u003ECharacteristics of patients in validation cohort in each of the four frailty categories based on one year risk of unplanned admission or one year risk of death. Values are numbers (column percentages) unless stated otherwise\u003C\/p\u003E\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022table-inline\u0022\u003E\u003Cdiv class=\u0022callout\u0022\u003E\u003Cspan\u003EView this table:\u003C\/span\u003E\u003Cul class=\u0022callout-links\u0022\u003E\u003Cli\u003E\u003Ca href=\u0022\/highwire\/markup\/951350\/expansion?width=1000\u0026amp;height=500\u0026amp;iframe=true\u0026amp;postprocessors=highwire_figures%2Chighwire_math\u0022 class=\u0022colorbox colorbox-load table-expand-popup\u0022 rel=\u0022gallery-fragment-tables\u0022\u003EView popup\u003C\/a\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca href=\u0022##\u0022 class=\u0022table-expand-inline\u0022 data-table-url=\u0022\/highwire\/markup\/951350\/expansion?postprocessors=highwire_figures%2Chighwire_math\u0022\u003EView inline\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022list-unord \u0022 id=\u0022list-2\u0022\u003E\u003Cli id=\u0022list-item-59\u0022\u003E\u003Cp id=\u0022p-125\u0022\u003EGroup 1 represents severe frailty. This category includes 13\u2009665 patients (ie, 2.74% of 499\u2009478) who are either in the top 2% at highest risk of death in the next year or in the top 2% at highest risk of unplanned hospital admission.\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-60\u0022\u003E\u003Cp id=\u0022p-126\u0022\u003EGroup 2 represents moderate frailty. This category includes 46\u2009770 patients (ie, 9.36% of 499\u2009478) who are either in the top 10% at highest risk of death in the next year or in the top 10% at highest risk of unplanned hospital admission (excluding those in the severe category).\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-61\u0022\u003E\u003Cp id=\u0022p-127\u0022\u003EGroup 3 represents mild frailty. This category includes 215\u2009253 patients (ie, 43.1% of 499\u2009478) who are either in the top 50% at highest risk of death in the next year or in the top 50% at highest risk of unplanned hospital admission (excluding those in the severe and moderate categories).\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-62\u0022\u003E\u003Cp id=\u0022p-128\u0022\u003EGroup 4 represents being \u201cfit.\u201d This category includes 223\u2009790 patients (ie, 44.80% of 499\u2009478) not in the above three categories.\u003C\/p\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Cp id=\u0022p-129\u0022\u003EFor example, for those in the severe frailty category, the mean age is 86.1 years, 98.5% (n=13\u2009460) have multimorbidity, 60.8% (n=8312) have poor mobility, 61.3% (n=8373) have cardiovascular disease, 50.5% (n=6895) have had falls, 46.2% (n=6310) have treated hypertension, 40.0% (n=5461) are taking antidepressants, 35.6% (n=4864) have atrial fibrillation, 34.8% (n=4756) have asthma or chronic obstructive pulmonary disease, 32.8% (n=4484) have dementia, 28.7% (n=3925) have type 2 diabetes, 25.1% (n=3436) have a diagnosis of cancer, 24.9% (n=3399) have anaemia, 19.9% (n=2724) have dyspnoea, 18.5% (n=2526) are taking anticoagulants, and 18.4% (n=2519) have peptic ulcer disease.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022section discussion\u0022 id=\u0022sec-23\u0022\u003E\u003Ch2 class=\u0022\u0022\u003EDiscussion\u003C\/h2\u003E\u003Cp id=\u0022p-130\u0022\u003ERecent NICE guidance on multiple morbidities\u003Ca id=\u0022xref-ref-3-6\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-3\u0022\u003E3\u003C\/a\u003E highlighted the need to develop new robust equations to identify patients in primary care with reduced life expectancy so that relevant assessments and interventions can be targeted appropriately. Existing equations to predict risk of death are based on biased samples, are insufficiently powered, fail to handle missing data appropriately, are poorly reported, or have poor performance to the extent that NICE has been unable to make a positive recommendation for any of the 41 models included in the review.\u003Ca id=\u0022xref-ref-3-7\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-3\u0022\u003E3\u003C\/a\u003E We therefore developed and validated equations to predict absolute risk of death over the next year in men and women aged 65-100 years. The QMortality equations performed well on a separate validation cohort, with good levels of discrimination and calibration, improving on other equations used to predict all cause mortality.\u003Ca id=\u0022xref-ref-2-7\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E\u003Ca id=\u0022xref-ref-3-8\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-3\u0022\u003E3\u003C\/a\u003E\u003Ca id=\u0022xref-ref-34-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-34\u0022\u003E34\u003C\/a\u003E The final model has good face validity as it includes demographic and clinical variables that clinicians would expect to affect mortality risk such as age, body mass index, deprivation, ethnicity, smoking status, alcohol intake, unplanned admissions in the past 12 months; atrial fibrillation, antipsychotics, cancer, asthma or chronic obstructive pulmonary disease, living in a care home, congestive heart failure, corticosteroids, cardiovascular disease, dementia, epilepsy, learning disability, leg ulcer, chronic liver disease or pancreatitis, Parkinson\u2019s disease, poor mobility, rheumatoid arthritis, chronic renal disease, type 1 diabetes, type 2 diabetes, venous thromboembolism, anaemia, abnormal liver function test result, high platelet count, and visits to a doctor in the past year with either appetite loss, unexpected weight loss, or breathlessness.\u003C\/p\u003E\u003Cp id=\u0022p-131\u0022\u003EAlthough the QMortality equation contains many variables, it is intended to be integrated into general practice computer systems where the extraction of data and risk calculation can be automated. We considered whether to develop a more parsimonious model with fewer predictors for use in other clinical settings but decided it would be preferable to have one model and for the user to select default values on the understanding that there may be a degree of under-estimation or over-estimation of risk depending on the predictor in question.\u003C\/p\u003E\u003Cdiv id=\u0022sec-24\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EPotential uses of the frailty classification and mortality index\u003C\/h3\u003E\u003Cp id=\u0022p-132\u0022\u003EIn this study we have described a specific novel use for mortality estimates, which is to classify patients into four frailty categories. This has been achieved by combining the one year predicted risk of death with the one year predicted risk of unplanned hospital admission to help identify the most severely frail patients for enhanced care packages to meet the immediate requirements of the UK General Medical Services contract. The most severe frailty category will identify patients with particularly high levels of morbidity who are at highest risk of death or unplanned hospital admission. This group of patients is likely to reflect elderly patients who are the most severely frail and who can be identified for focused assessment and intervention as part of the new General Medical Services contract in England. This includes falls assessment and drug review. The QMortality score could be used in conjunction with the QAdmissions score to allocate patients to one of four QFrailty categories. It could also be used recurrently to build and maintain practice based lists of patients with different levels of frailty or mortality risks over time. This could be done as an automated procedure using electronic health records.\u003C\/p\u003E\u003Cp id=\u0022p-133\u0022\u003EThe models can also be used in a face-to-face consultation between the patient and clinician with the intention of sharing the information with the patient to assess management options. The decision curve analysis shows there is a higher net benefit for the prediction models than strategies based on considering either no patients or all patients for intervention for risk thresholds up to around 50%. Mortality estimates including cancer stage and grade are already used to help patients with cancer to weigh up the risks and benefits of surgery, chemotherapy, and radiotherapy.\u003Ca id=\u0022xref-ref-35-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-35\u0022\u003E35\u003C\/a\u003E Patients with a high risk of death in the near future may choose to decline aggressive treatments or defer preventive treatments, screening interventions, or interventions for asymptomatic conditions.\u003Ca id=\u0022xref-ref-36-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-36\u0022\u003E36\u003C\/a\u003E Mortality estimates could also be used to help guide the introduction and addition of palliative care to help plan end of life care.\u003Ca id=\u0022xref-ref-37-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-37\u0022\u003E37\u003C\/a\u003E For example, six month mortality estimates in the United States are used to trigger Advance Care Planning and also to determine access to hospice services under the Medicare scheme.\u003Ca id=\u0022xref-ref-38-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-38\u0022\u003E38\u003C\/a\u003E They are also used to improve self awareness of health status; to measure, monitor, and compare outcomes between different healthcare providers\u003Ca id=\u0022xref-ref-36-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-36\u0022\u003E36\u003C\/a\u003E; and are used by governments to decrease the burden of certain risk factors at a population level.\u003Ca id=\u0022xref-ref-34-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-34\u0022\u003E34\u003C\/a\u003E\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-25\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EEthical considerations\u003C\/h3\u003E\u003Cp id=\u0022p-134\u0022\u003EWe see an important distinction between factors that are included in a risk equation to ensure that the risk estimates are as accurate as possible and how the risk equation is then used in guidelines and clinical practice to ensure ethical, effective, and equitable access to services for everyone. The primary purpose of our paper is to report on the development and validation of new risk equations rather than to produce national policy or clinical guidance, although we recognise the results may be used by policy makers and clinicians. All clinical decisions about the beneficial and safe use of these risk equations necessarily remain the responsibility of the attending clinician. However, there are ethical issues to consider about how the tools might be used. We have analysed this within the \u201cfour ethical principles\u201d framework, which is widely used in medical decision making. The four principles are autonomy, beneficence, justice, and non-maleficence.\u003Ca id=\u0022xref-ref-39-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-39\u0022\u003E39\u003C\/a\u003E The new risk equations, when implemented in clinical software, are designed to provide more accurate information for patients and clinicians on which to base decisions, thereby promoting shared decision making and patient autonomy. They are intended to result in clinical benefit by identifying where changes in management are likely to benefit patients, thereby promoting the principle of beneficence. Justice can be achieved by ensuring that the use of the risk equations results in fair and equitable access to health services that are commensurate with the patients\u2019 level of risk. Lastly, the risk assessment must not be used in a way that causes harm either to the individual patient or to others (for example, by introducing or withdrawing treatments where this is not in the patients\u2019 best interest) thereby supporting the non-maleficence principle. How this applies in clinical practice will naturally depend on many factors, especially the patient\u2019s wishes, the evidence base for any interventions, the clinician\u2019s experience, national priorities, and the available resources. The risk assessment equations therefore supplement clinical decision making, not replace it.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-26\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EComparison with the other risk scores\u003C\/h3\u003E\u003Cp id=\u0022p-135\u0022\u003EA recent review of 41 mortality risk scores reported in 24 research papers failed to identify any that could be reliably used to predict mortality in a community settitng.\u003Ca id=\u0022xref-ref-3-9\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-3\u0022\u003E3\u003C\/a\u003E Of the studies reviewed, the Charleston comorbidity index, which consists of 23 variables, achieved the best C statistic, with a value of 0.77 in the internal validation cohort and 0.80 in the external validation cohort.\u003Ca id=\u0022xref-ref-3-10\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-3\u0022\u003E3\u003C\/a\u003E\u003Ca id=\u0022xref-ref-40-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-40\u0022\u003E40\u003C\/a\u003E Other studies have used risk scores to predict mortality, such as the John Hopkins Aggregated Diagnostic Groups (ADG) score\u003Ca id=\u0022xref-ref-41-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-41\u0022\u003E41\u003C\/a\u003E and the Hospital-patient One-year Mortality Risk (HOMR) score.\u003Ca id=\u0022xref-ref-36-3\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-36\u0022\u003E36\u003C\/a\u003E The HOMR score consists of 12 patient variables and eight hospital admission factors and was designed to predict one year mortality risk in adults aged 18-100 years admitted to hospital. It includes fractional polynomial terms for continuous variables and interactions between statistically significant predictors. The HOMR score has excellent calibration and discrimination, with a C statistic of 0.92, although this may reflect the much wider age range in the HOMR study. The ADG score consists of 30 variables and has been validated using a community based sample. However, the C statistic was lower (0.81) than the values for the QMortality score (0.84 in men and 0.85 in women), and the ADG equation is not published or freely available.\u003Ca id=\u0022xref-ref-41-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-41\u0022\u003E41\u003C\/a\u003E\u003C\/p\u003E\u003Cp id=\u0022p-136\u0022\u003EThe electronic frailty index (EFI) is a simple unweighted count of the number of \u201cdeficits\u201d a patient has out of a total of 36, where a deficit is a physical disability or social vulnerability as identified by a consensus panel.\u003Ca id=\u0022xref-ref-2-8\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E The EFI has also been used to predict mortality in a UK community based population, although performance (based on standard definitions\u003Ca id=\u0022xref-ref-42-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-42\u0022\u003E42\u003C\/a\u003E) was poor or fair, with a C statistic of 0.66 on an internal validation cohort and 0.76 on an external validation cohort.\u003Ca id=\u0022xref-ref-2-9\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E The EFI also had extremely low levels of explained variation in time to death of 0.02-0.04%,\u003Ca id=\u0022xref-ref-2-10\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-2\u0022\u003E2\u003C\/a\u003E whereas the QMortality scores explained 53% and 55% of the variation in men and women, respectively. The EFI equation has not been published and it does not appear to include continuous variables such as age. The QMortality and QAdmissions equations include all the factors in the EFI, where these predicted either risk of death or risk of unplanned hospital admission. Unlike the EFI, our equations include further key determinants of death and unplanned admissions, such as age, sex, ethnic group, smoking status, alcohol intake, deprivation, and previous unplanned admissions, and also include major conditions\u2014cancer, epilepsy, serious mental illness, chronic liver disease, inflammatory bowel disease, learning disability, specific drug treatments\u2014which are all relevant to risk of outcomes and for which patients are likely to need ongoing careful assessment. Our multivariable analysis has allowed us to attribute appropriate weights to each factor and incorporate interactions between age and different medical conditions. This means, for example, that a patient who is 65 years old with three medical conditions will have a different absolute risk of death or unplanned hospital admission than a patient with the same conditions but who is aged 95 years.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-27\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EMethodological considerations\u003C\/h3\u003E\u003Cp id=\u0022p-137\u0022\u003EThe methods to derive and validate these models are broadly the same as for a range of other clinical risk prediction tools derived from the QResearch database.\u003Ca id=\u0022xref-ref-7-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-7\u0022\u003E7\u003C\/a\u003E\u003Ca id=\u0022xref-ref-8-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-8\u0022\u003E8\u003C\/a\u003E\u003Ca id=\u0022xref-ref-12-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-12\u0022\u003E12\u003C\/a\u003E\u003Ca id=\u0022xref-ref-43-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-43\u0022\u003E43\u003C\/a\u003E\u003Ca id=\u0022xref-ref-44-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-44\u0022\u003E44\u003C\/a\u003E The strengths and limitations of the approach have already been discussed in detail.\u003Ca id=\u0022xref-ref-7-3\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-7\u0022\u003E7\u003C\/a\u003E\u003Ca id=\u0022xref-ref-14-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-14\u0022\u003E14\u003C\/a\u003E\u003Ca id=\u0022xref-ref-43-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-43\u0022\u003E43\u003C\/a\u003E\u003Ca id=\u0022xref-ref-45-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-45\u0022\u003E45\u003C\/a\u003E\u003Ca id=\u0022xref-ref-46-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-46\u0022\u003E46\u003C\/a\u003E\u003Ca id=\u0022xref-ref-47-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-47\u0022\u003E47\u003C\/a\u003E In summary, key strengths include size, duration of follow-up, representativeness, and lack of selection, recall, and respondent bias. UK general practices have good levels of accuracy and completeness in recording clinical diagnoses and prescribed drugs.\u003Ca id=\u0022xref-ref-48-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-48\u0022\u003E48\u003C\/a\u003E We think our study has good face validity since it has been conducted in the setting where most patients in the UK are assessed, treated, and followed up. Limitations of our study include the lack of formal adjudication of diagnoses, information bias, and potential for bias owing to missing data. Our database has linked hospital admissions data and is therefore likely to have picked up the majority of hospital admissions, thereby minimising ascertainment bias. We focused on two hard outcomes to identify frail patients (unplanned admissions and mortality) rather than admission to a nursing home or decline in function, as both of these are more difficult to measure using electronic health records. Also, for simplicity we grouped all cancers together as a single variable rather than distinguish between different types of cancer and account for grade and stage. This was a pragmatic decision, partly driven by the lack of information in general practice records about grade and stage of cancer and the availability of existing purpose designed tools such as the QCancer prognostic scores.\u003Ca id=\u0022xref-ref-49-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-49\u0022\u003E49\u003C\/a\u003E QMortality will tend to have under-estimated mortality risk in those with a late stage cancer (for example) and over-estimated it in patients with an early stage cancer. We excluded patients without a deprivation score since this group may represent a more transient population where follow-up could be unreliable or unrepresentative. Their deprivation scores are unlikely to be missing at random so we did not think it would be appropriate to impute them.\u003C\/p\u003E\u003Cp id=\u0022p-138\u0022\u003EWe have presented sensitivity and specificity values for death at a range of centile values and combined predicted risks of death and unplanned hospital admissions into frailty categories that can be used to identify patients who are most severely frail based on their risk of clinically important outcomes. The present validation has been done on a separate set of practices and individuals to those that were used to develop the score, although the practices all use the same general practice clinical computer system (EMIS, used by 55% of UK general practitioners). An independent validation study would be a more stringent test and should be done, but when such independent studies have examined other risk equations\u003Ca id=\u0022xref-ref-46-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-46\u0022\u003E46\u003C\/a\u003E\u003Ca id=\u0022xref-ref-47-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-47\u0022\u003E47\u003C\/a\u003E\u003Ca id=\u0022xref-ref-50-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-50\u0022\u003E50\u003C\/a\u003E\u003Ca id=\u0022xref-ref-51-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-51\u0022\u003E51\u003C\/a\u003E they have shown similar performance compared with the validation in the QResearch database.\u003Ca id=\u0022xref-ref-12-3\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-12\u0022\u003E12\u003C\/a\u003E\u003Ca id=\u0022xref-ref-43-3\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-43\u0022\u003E43\u003C\/a\u003E\u003Ca id=\u0022xref-ref-45-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-45\u0022\u003E45\u003C\/a\u003E We have not been able to undertake direct comparisons between the QMortality score and the ADG, EFI, and HOMR scores since these are not publicly available. For transparency, we have published the source code of the QMortality equation on the QAdmissions website (\u003Ca href=\u0022http:\/\/www.qadmissions.org\u0022\u003Ewww.qadmissions.org\u003C\/a\u003E) alongside the QAdmissions equation. The rationale for this is to ensure that those interested in reviewing or using the open source will then be able to find the latest available version as the score continues to be updated. Lastly, our study was not designed to compare the performance of QMortality scores against clinical judgment alone, although we have provided sufficient information to enable other researchers to undertake such a study. Freund et al found that predictive modelling software was more effective at identifying patients at increased risk of hospital admission and death than clinical judgment alone.\u003Ca id=\u0022xref-ref-52-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-52\u0022\u003E52\u003C\/a\u003E However, clinicians may be more effective at identifying those for whom preventive services may have a better impact.\u003Ca id=\u0022xref-ref-52-2\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-52\u0022\u003E52\u003C\/a\u003E\u003Ca id=\u0022xref-ref-53-1\u0022 class=\u0022xref-bibr\u0022 href=\u0022#ref-53\u0022\u003E53\u003C\/a\u003E\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-28\u0022 class=\u0022subsection\u0022\u003E\u003Ch3\u003EConclusion\u003C\/h3\u003E\u003Cp id=\u0022p-139\u0022\u003EWe have developed a new equation to quantify absolute risk of death within the next year in people aged 65 or more, taking account of demographic, social, and clinical variables. The equation provides a valid measure of absolute risk of death in the general population of patients aged 65 or more as shown by the performance in a separate validation cohort. The equation can be used in conjunction with the QAdmissions equation to classify patients into four QFrailty groups to enable their identification for focused assessments and interventions.\u003C\/p\u003E\u003Cdiv class=\u00224\u0022 id=\u0022boxed-text-2\u0022\u003E\u003Cdiv id=\u0022sec-29\u0022 class=\u0022subsection\u0022\u003E\u003Ch4\u003EWhat is already known on this topic\u003C\/h4\u003E\u003Cul class=\u0022list-simple \u0022 id=\u0022list-3\u0022\u003E\u003Cli id=\u0022list-item-63\u0022\u003E\u003Cp id=\u0022p-140\u0022\u003ERecent NICE guidance on multiple morbidities has highlighted the need to develop new robust equations to identify patients in primary care with reduced life expectancy so that relevant assessments and interventions can be targeted appropriately\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-64\u0022\u003E\u003Cp id=\u0022p-141\u0022\u003EExisting equations to predict risk of death are based on biased samples, are insufficiently powered, fail to handle missing data appropriately, are poorly reported, or have poor performance to the extent that NICE has been unable to make a positive recommendation on any tool\u003C\/p\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv id=\u0022sec-30\u0022 class=\u0022subsection\u0022\u003E\u003Ch4\u003EWhat this study adds\u003C\/h4\u003E\u003Cul class=\u0022list-simple \u0022 id=\u0022list-4\u0022\u003E\u003Cli id=\u0022list-item-65\u0022\u003E\u003Cp id=\u0022p-142\u0022\u003EA new equation (QMortality) quantified absolute risk of death within the next one year in people aged 65 or more, taking account of demographic, social, and clinical variables\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-66\u0022\u003E\u003Cp id=\u0022p-143\u0022\u003EQMortality provides a valid measure of absolute risk of death in the general population of older patients, as shown by its performance in a separate validation cohort\u003C\/p\u003E\u003C\/li\u003E\u003Cli id=\u0022list-item-67\u0022\u003E\u003Cp id=\u0022p-144\u0022\u003EQMortality can be used in conjunction with the QAdmissions equation for unplanned hospital admissions to classify patients into four QFrailty groups to enable identification for focused assessments and interventions\u003C\/p\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022section fn-group\u0022 id=\u0022fn-group-1\u0022\u003E\u003Ch2\u003EFootnotes\u003C\/h2\u003E\u003Cul\u003E\u003Cli class=\u0022fn\u0022 id=\u0022fn-1\u0022\u003E\u003Cp id=\u0022p-145\u0022\u003EA simple web calculator can be used to implement the QMortality algorithm (\u003Ca href=\u0022http:\/\/qmortality.org\u0022\u003Ehttp:\/\/qmortality.org\u003C\/a\u003E), which will be publicly available alongside the paper. It also has the open source software for download. A web calculator to implement the combined QMortality and QAdmissions calculator to derive the four frailty categories is available here \u003Ca href=\u0022http:\/\/qfrailty.org\u0022\u003Ehttp:\/\/qfrailty.org\u003C\/a\u003E.\u003C\/p\u003E\u003Cp id=\u0022p-146\u0022\u003EWe acknowledge the contribution of EMIS practices who contribute to the QResearch database and EMIS and the University of Nottingham for expertise in establishing, developing, and supporting the QResearch database. The hospital episodes statistics data used in this analysis are re-used by permission from the NHS Digital who retain the copyright. We thank the Office for National Statistics for providing the mortality data. ONS and NHS Digital bear no responsibility for the analysis or interpretation of the data\u003C\/p\u003E\u003C\/li\u003E\u003Cli class=\u0022fn-participating-researchers\u0022 id=\u0022fn-2\u0022\u003E\u003Cp id=\u0022p-147\u0022\u003EContributors: JHC initiated the study; developed the research question; undertook the literature review, data extraction, data manipulation, and primary data analysis; and wrote the first draft of the paper. CC contributed to the refinement of the research question, design, analysis, interpretation, and drafting of the paper. JHC is the guarantor for this study.\u003C\/p\u003E\u003C\/li\u003E\u003Cli class=\u0022fn-financial-disclosure\u0022 id=\u0022fn-3\u0022\u003E\u003Cp id=\u0022p-148\u0022\u003EFunding: There was no external funding for this study.\u003C\/p\u003E\u003C\/li\u003E\u003Cli class=\u0022fn-conflict\u0022 id=\u0022fn-4\u0022\u003E\u003Cp id=\u0022p-149\u0022\u003ECompeting interests: Both authors have completed the uniform disclosure form at \u003Ca href=\u0022http:\/\/www.icmje.org\/coi_disclosure.pdf\u0022\u003Ewww.icmje.org\/coi_disclosure.pdf\u003C\/a\u003E (available on request from the corresponding author) and declare: JHC is codirector of QResearch, a not-for-profit organisation, which is a joint partnership between the University of Nottingham and Egton Medical Information Systems (leading commercial supplier of IT for 55% of general practices in the UK). JHC is also a paid director of ClinRisk, which produces open and closed source software to ensure the reliable and updatable implementation of clinical risk equations within clinical computer systems to help improve patient care. CC is a paid consultant statistician for ClinRisk. This work and any views expressed within it are solely those of the authors and not of any affiliated bodies or organisations.\u003C\/p\u003E\u003C\/li\u003E\u003Cli class=\u0022fn-other\u0022 id=\u0022fn-5\u0022\u003E\u003Cp id=\u0022p-150\u0022\u003EEthical approval: This study was approved by the East Midlands Derby Research Ethics Committee (reference 03\/4\/021).\u003C\/p\u003E\u003C\/li\u003E\u003Cli class=\u0022fn-other\u0022 id=\u0022fn-6\u0022\u003E\u003Cp id=\u0022p-151\u0022\u003EData sharing: The equations presented in this paper will be released as Open Source Software under the GNU lesser GPL v3. The open source software allows use without charge under the terms of the GNU lesser public license version 3. Closed source software can be licensed at a fee.\u003C\/p\u003E\u003C\/li\u003E\u003Cli class=\u0022fn-other\u0022 id=\u0022fn-7\u0022\u003E\u003Cp id=\u0022p-152\u0022\u003ETransparency: The lead author (JHC) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.\u003C\/p\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022license\u0022 id=\u0022license-1\u0022\u003E\u003Cp id=\u0022p-1\u0022\u003EThis is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: \u003Ca href=\u0022http:\/\/creativecommons.org\/licenses\/by\/4.0\/\u0022 rel=\u0022license\u0022\u003Ehttp:\/\/creativecommons.org\/licenses\/by\/4.0\/\u003C\/a\u003E.\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv class=\u0022section ref-list\u0022 id=\u0022ref-list-1\u0022\u003E\u003Ch2\u003EReferences\u003C\/h2\u003E\u003Col class=\u0022cit-list\u0022\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-1-1\u0022 title=\u0022View reference 1 in text\u0022 id=\u0022ref-1\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.1\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ESearle\u00a0SD,\u00a0Mitnitski\u00a0A,\u00a0Gahbauer\u00a0EA,\u00a0Gill\u00a0TM,\u00a0Rockwood\u00a0K. A standard procedure for creating a frailty index. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMC Geriatr\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2008\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E24\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1186\/1471-2318-8-24\u0022\u003Edoi:10.1186\/1471-2318-8-24\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=18826625\u0022\u003Epmid:18826625\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMC%2BGeriatr%26rft.volume%253D358%26rft.spage%253D24%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-2-1\u0022 title=\u0022View reference 2 in text\u0022 id=\u0022ref-2\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.2\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EClegg\u00a0A,\u00a0Bates\u00a0C,\u00a0Young\u00a0J,\u00a0et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EAge Ageing\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2016\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E353\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E60\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1093\/ageing\/afw039\u0022\u003Edoi:10.1093\/ageing\/afw039\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=26944937\u0022\u003Epmid:26944937\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DAge%2BAgeing%26rft.volume%253D358%26rft.spage%253D353%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-3-1\u0022 title=\u0022View reference 3 in text\u0022 id=\u0022ref-3\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-book\u0022 id=\u0022cit-358.sep20_12.j4208.3\u0022\u003E\u003Cdiv class=\u0022cit-metadata unstructured\u0022\u003ENational Institute for Clinical Excellence. Multimorbidity: clinical assessment and management, NICE guidelines NG56. In: NICE, ed. London, 2016:443.\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-4-1\u0022 title=\u0022View reference 4 in text\u0022 id=\u0022ref-4\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.4\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C. Predicting risk of emergency admission to hospital using primary care data: derivation and validation of QAdmissions score. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ Open\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2013\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ee003482\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmjopen-2013-003482\u0022\u003Edoi:10.1136\/bmjopen-2013-003482\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=23959760\u0022\u003Epmid:23959760\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%2BOpen%26rft.volume%253D358%26rft.spage%253De003482%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-5-1\u0022 title=\u0022View reference 5 in text\u0022 id=\u0022ref-5\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.5\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EDonnan\u00a0PT,\u00a0Dorward\u00a0DWT,\u00a0Mutch\u00a0B,\u00a0Morris\u00a0AD. Development and validation of a model for predicting emergency admissions over the next year (PEONY): a UK historical cohort study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EArch Intern Med\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2008\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E1416\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E22\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1001\/archinte.168.13.1416\u0022\u003Edoi:10.1001\/archinte.168.13.1416\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=18625922\u0022\u003Epmid:18625922\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DArch%2BIntern%2BMed%26rft.volume%253D358%26rft.spage%253D1416%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-6-1\u0022 title=\u0022View reference 6 in text\u0022 id=\u0022ref-6\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-web\u0022 id=\u0022cit-358.sep20_12.j4208.6\u0022\u003E\u003Cdiv class=\u0022cit-metadata unstructured\u0022\u003EHippisley-Cox J. Validity and completeness of the NHS number in primary and secondary care: electronic data in England 1991-2013. 2013. \u003Ca href=\u0022http:\/\/eprints.nottingham.ac.uk\/3153\/1\/Validity%26CompletenessNHSNumber.pdf\u0022\u003Ehttp:\/\/eprints.nottingham.ac.uk\/3153\/1\/Validity%26CompletenessNHSNumber.pdf\u003C\/a\u003E (accessed June 2013).\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-7-1\u0022 title=\u0022View reference 7 in text\u0022 id=\u0022ref-7\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.7\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C. Development and validation of risk prediction algorithm (QThrombosis) to estimate future risk of venous thromboembolism: prospective cohort study\u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2011\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ed4656\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.d4656\u0022\u003Edoi:10.1136\/bmj.d4656\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=21846713\u0022\u003Epmid:21846713\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Dd4656%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-8-1\u0022 title=\u0022View reference 8 in text\u0022 id=\u0022ref-8\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.8\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C. Derivation and validation of updated QFracture algorithm to predict risk of osteoporotic fracture in primary care in the United Kingdom: prospective open cohort study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2012\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ee3427\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.e3427\u0022\u003Edoi:10.1136\/bmj.e3427\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=22619194\u0022\u003Epmid:22619194\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253De3427%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-9-1\u0022 title=\u0022View reference 9 in text\u0022 id=\u0022ref-9\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.9\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C. Symptoms and risk factors to identify men with suspected cancer in primary care: derivation and validation of an algorithm. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBr J Gen Pract\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2013\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ee1\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E10\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.3399\/bjgp13X660724\u0022\u003Edoi:10.3399\/bjgp13X660724\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=23336443\u0022\u003Epmid:23336443\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBr%2BJ%2BGen%2BPract%26rft.volume%253D358%26rft.spage%253De1%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-10-1\u0022 title=\u0022View reference 10 in text\u0022 id=\u0022ref-10\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.10\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C. Symptoms and risk factors to identify women with suspected cancer in primary care: derivation and validation of an algorithm. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBr J Gen Pract\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2013\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ee11\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E21\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.3399\/bjgp13X660733\u0022\u003Edoi:10.3399\/bjgp13X660733\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=23336450\u0022\u003Epmid:23336450\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBr%2BJ%2BGen%2BPract%26rft.volume%253D358%26rft.spage%253De11%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-11-1\u0022 title=\u0022View reference 11 in text\u0022 id=\u0022ref-11\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-book\u0022 id=\u0022cit-358.sep20_12.j4208.11\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ETownsend\u00a0P,\u00a0Davidson\u00a0N. \u003Cspan class=\u0022cit-source\u0022\u003EThe Black report\u003C\/span\u003E. Penguin, \u003Cspan class=\u0022cit-pub-date\u0022\u003E1982\u003C\/span\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-12-1\u0022 title=\u0022View reference 12 in text\u0022 id=\u0022ref-12\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.12\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C,\u00a0Vinogradova\u00a0Y,\u00a0et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2008\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E1475\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E82\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.39609.449676.25\u0022\u003Edoi:10.1136\/bmj.39609.449676.25\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=18573856\u0022\u003Epmid:18573856\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253D1475%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-13-1\u0022 title=\u0022View reference 13 in text\u0022 id=\u0022ref-13\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.13\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C,\u00a0Vinogradova\u00a0Y,\u00a0Robson\u00a0J,\u00a0May\u00a0M,\u00a0Brindle\u00a0P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2007\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E136\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.39261.471806.55\u0022\u003Edoi:10.1136\/bmj.39261.471806.55\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=17615182\u0022\u003Epmid:17615182\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253D136%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-14-1\u0022 title=\u0022View reference 14 in text\u0022 id=\u0022ref-14\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.14\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C,\u00a0Vinogradova\u00a0Y,\u00a0Robson\u00a0J,\u00a0Brindle\u00a0P. Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EHeart\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2008\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E34\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E9\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/hrt.2007.134890\u0022\u003Edoi:10.1136\/hrt.2007.134890\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=17916661\u0022\u003Epmid:17916661\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DHeart%26rft.volume%253D358%26rft.spage%253D34%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-15-1\u0022 title=\u0022View reference 15 in text\u0022 id=\u0022ref-15\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.15\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ECollins\u00a0GS,\u00a0Altman\u00a0DG. An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2009\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Eb2584\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.b2584\u0022\u003Edoi:10.1136\/bmj.b2584\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=19584409\u0022\u003Epmid:19584409\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Db2584%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-16-1\u0022 title=\u0022View reference 16 in text\u0022 id=\u0022ref-16\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.16\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ESchafer\u00a0JL,\u00a0Graham\u00a0JW. Missing data: our view of the state of the art. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EPsychol Methods\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2002\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E147\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E77\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1037\/1082-989X.7.2.147\u0022\u003Edoi:10.1037\/1082-989X.7.2.147\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=12090408\u0022\u003Epmid:12090408\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DPsychol%2BMethods%26rft.volume%253D358%26rft.spage%253D147%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-17-1\u0022 title=\u0022View reference 17 in text\u0022 id=\u0022ref-17\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.17\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EWhite\u00a0IR,\u00a0Royston\u00a0P,\u00a0Wood\u00a0AM. Multiple imputation using chained equations: Issues and guidance for practice. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EStat Med\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2011\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E377\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E99\u003C\/span\u003E.\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=21225900\u0022\u003Epmid:21225900\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DStat%2BMed%26rft.volume%253D358%26rft.spage%253D377%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-18-1\u0022 title=\u0022View reference 18 in text\u0022 id=\u0022ref-18\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.18\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ESteyerberg\u00a0EW,\u00a0van Veen\u00a0M. Imputation is beneficial for handling missing data in predictive models. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EJ Clin Epidemiol\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2007\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E979\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1016\/j.jclinepi.2007.03.003\u0022\u003Edoi:10.1016\/j.jclinepi.2007.03.003\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=17689816\u0022\u003Epmid:17689816\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DJ%2BClin%2BEpidemiol%26rft.volume%253D358%26rft.spage%253D979%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-19-1\u0022 title=\u0022View reference 19 in text\u0022 id=\u0022ref-19\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.19\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EMoons\u00a0KGM,\u00a0Donders\u00a0RART,\u00a0Stijnen\u00a0T,\u00a0Harrell\u00a0FE\u00a0Jr. Using the outcome for imputation of missing predictor values was preferred. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EJ Clin Epidemiol\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2006\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E1092\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E101\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1016\/j.jclinepi.2006.01.009\u0022\u003Edoi:10.1016\/j.jclinepi.2006.01.009\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=16980150\u0022\u003Epmid:16980150\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DJ%2BClin%2BEpidemiol%26rft.volume%253D358%26rft.spage%253D1092%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-20-1\u0022 title=\u0022View reference 20 in text\u0022 id=\u0022ref-20\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.20\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ESchafer\u00a0JL. Multiple imputation: a primer. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EStat Methods Med Res\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E1999\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E3\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E15\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1177\/096228029900800102\u0022\u003Edoi:10.1177\/096228029900800102\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=10347857\u0022\u003Epmid:10347857\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DStat%2BMethods%2BMed%2BRes%26rft.volume%253D358%26rft.spage%253D3%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-21-1\u0022 title=\u0022View reference 21 in text\u0022 id=\u0022ref-21\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-book\u0022 id=\u0022cit-358.sep20_12.j4208.21\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ERubin\u00a0DB. \u003Cspan class=\u0022cit-source\u0022\u003EMultiple Imputation for Non-response in Surveys.\u003C\/span\u003EJohn Wiley, \u003Cspan class=\u0022cit-pub-date\u0022\u003E1987\u003C\/span\u003E\u003Ca href=\u0022http:\/\/dx.doi.org\/10.1002\/9780470316696\u0022\u003Edoi:10.1002\/9780470316696\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-22-1\u0022 title=\u0022View reference 22 in text\u0022 id=\u0022ref-22\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.22\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ERoyston\u00a0P,\u00a0Ambler\u00a0G,\u00a0Sauerbrei\u00a0W. The use of fractional polynomials to model continuous risk variables in epidemiology. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EInt J Epidemiol\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E1999\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E964\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E74\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1093\/ije\/28.5.964\u0022\u003Edoi:10.1093\/ije\/28.5.964\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=10597998\u0022\u003Epmid:10597998\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DInt%2BJ%2BEpidemiol%26rft.volume%253D358%26rft.spage%253D964%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-23-1\u0022 title=\u0022View reference 23 in text\u0022 id=\u0022ref-23\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-book\u0022 id=\u0022cit-358.sep20_12.j4208.23\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHosmer\u00a0D,\u00a0Lemeshow\u00a0S,\u00a0May\u00a0S. \u003Cspan class=\u0022cit-source\u0022\u003EApplied Survival Analysis: Regression Modelling of Time to Event data\u003C\/span\u003E. John Wiley, \u003Cspan class=\u0022cit-pub-date\u0022\u003E1999\u003C\/span\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-24-1\u0022 title=\u0022View reference 24 in text\u0022 id=\u0022ref-24\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.24\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C,\u00a0Brindle\u00a0P. The performance of seven QPrediction risk scores in an independent external sample of patients from general practice: a validation study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ Open\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2014\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ee005809\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmjopen-2014-005809\u0022\u003Edoi:10.1136\/bmjopen-2014-005809\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=25168040\u0022\u003Epmid:25168040\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%2BOpen%26rft.volume%253D358%26rft.spage%253De005809%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-25-1\u0022 title=\u0022View reference 25 in text\u0022 id=\u0022ref-25\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.25\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ERoyston\u00a0P. Explained variation for survival models. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EStata J\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2006\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E1\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E14\u003C\/span\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DStata%2BJ%26rft.volume%253D358%26rft.spage%253D1%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-26-1\u0022 title=\u0022View reference 26 in text\u0022 id=\u0022ref-26\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.26\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ERoyston\u00a0P,\u00a0Sauerbrei\u00a0W. A new measure of prognostic separation in survival data. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EStat Med\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2004\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E723\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E48\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1002\/sim.1621\u0022\u003Edoi:10.1002\/sim.1621\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=14981672\u0022\u003Epmid:14981672\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DStat%2BMed%26rft.volume%253D358%26rft.spage%253D723%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-27-1\u0022 title=\u0022View reference 27 in text\u0022 id=\u0022ref-27\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.27\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHarrell\u00a0FE\u00a0Jr,\u00a0,\u00a0Lee\u00a0KL,\u00a0Mark\u00a0DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EStat Med\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E1996\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E361\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E87\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1002\/(SICI)1097-0258(19960229)15:4\u0026lt;361::AID-SIM168\u0026gt;3.0.CO;2-4\u0022\u003Edoi:10.1002\/(SICI)1097-0258(19960229)15:4\u0026lt;361::AID-SIM168\u0026gt;3.0.CO;2-4\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=8668867\u0022\u003Epmid:8668867\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DStat%2BMed%26rft.volume%253D358%26rft.spage%253D361%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-28-1\u0022 title=\u0022View reference 28 in text\u0022 id=\u0022ref-28\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.28\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ENewson\u00a0RB. Comparing the predictive powers of survival models using Harrell\u2019s C or Somers\u2019 D. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EStata J\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2010\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E339\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E58\u003C\/span\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DStata%2BJ%26rft.volume%253D358%26rft.spage%253D339%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-29-1\u0022 title=\u0022View reference 29 in text\u0022 id=\u0022ref-29\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.29\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ERiley\u00a0RD,\u00a0Ensor\u00a0J,\u00a0Snell\u00a0KIE,\u00a0et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2016\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ei3140\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.i3140\u0022\u003Edoi:10.1136\/bmj.i3140\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=27334381\u0022\u003Epmid:27334381\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Di3140%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-30-1\u0022 title=\u0022View reference 30 in text\u0022 id=\u0022ref-30\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.30\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ESteyerberg\u00a0EW,\u00a0Vickers\u00a0AJ,\u00a0Cook\u00a0NR,\u00a0et al. Assessing the performance of prediction models: a framework for traditional and novel measures. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EEpidemiology\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2010\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E128\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E38\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1097\/EDE.0b013e3181c30fb2\u0022\u003Edoi:10.1097\/EDE.0b013e3181c30fb2\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=20010215\u0022\u003Epmid:20010215\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DEpidemiology%26rft.volume%253D358%26rft.spage%253D128%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-31-1\u0022 title=\u0022View reference 31 in text\u0022 id=\u0022ref-31\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.31\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EVickers\u00a0AJ,\u00a0Van Calster\u00a0B,\u00a0Steyerberg\u00a0EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2016\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ei6\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.i6\u0022\u003Edoi:10.1136\/bmj.i6\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=26810254\u0022\u003Epmid:26810254\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Di6%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-32-1\u0022 title=\u0022View reference 32 in text\u0022 id=\u0022ref-32\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.32\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EVickers\u00a0AJ,\u00a0Elkin\u00a0EB. Decision curve analysis: a novel method for evaluating prediction models. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EMed Decis Making\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2006\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E565\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E74\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1177\/0272989X06295361\u0022\u003Edoi:10.1177\/0272989X06295361\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=17099194\u0022\u003Epmid:17099194\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DMed%2BDecis%2BMaking%26rft.volume%253D358%26rft.spage%253D565%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-33-1\u0022 title=\u0022View reference 33 in text\u0022 id=\u0022ref-33\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.33\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ECollins\u00a0GS,\u00a0Reitsma\u00a0JB,\u00a0Altman\u00a0DG,\u00a0Moons\u00a0KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EAnn Intern Med\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2015\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E55\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E63\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.7326\/M14-0697\u0022\u003Edoi:10.7326\/M14-0697\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=25560714\u0022\u003Epmid:25560714\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DAnn%2BIntern%2BMed%26rft.volume%253D358%26rft.spage%253D55%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-34-1\u0022 title=\u0022View reference 34 in text\u0022 id=\u0022ref-34\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.34\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EGanna\u00a0A,\u00a0Ingelsson\u00a0E. 5 year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003ELancet\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2015\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E533\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E40\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1016\/S0140-6736(15)60175-1\u0022\u003Edoi:10.1016\/S0140-6736(15)60175-1\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=26049253\u0022\u003Epmid:26049253\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DLancet%26rft.volume%253D358%26rft.spage%253D533%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-35-1\u0022 title=\u0022View reference 35 in text\u0022 id=\u0022ref-35\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.35\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EReljic\u00a0T,\u00a0Kumar\u00a0A,\u00a0Klocksieben\u00a0FA,\u00a0Djulbegovic\u00a0B. Treatment targeted at underlying disease versus palliative care in terminally ill patients: a systematic review. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ Open\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2017\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ee014661\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmjopen-2016-014661\u0022\u003Edoi:10.1136\/bmjopen-2016-014661\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=28062473\u0022\u003Epmid:28062473\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%2BOpen%26rft.volume%253D358%26rft.spage%253De014661%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-36-1\u0022 title=\u0022View reference 36 in text\u0022 id=\u0022ref-36\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.36\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003Evan Walraven\u00a0C. The Hospital-patient One-year Mortality Risk score accurately predicted long-term death risk in hospitalized patients. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EJ Clin Epidemiol\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2014\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E1025\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E34\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1016\/j.jclinepi.2014.05.003\u0022\u003Edoi:10.1016\/j.jclinepi.2014.05.003\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=24973823\u0022\u003Epmid:24973823\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DJ%2BClin%2BEpidemiol%26rft.volume%253D358%26rft.spage%253D1025%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-37-1\u0022 title=\u0022View reference 37 in text\u0022 id=\u0022ref-37\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.37\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EDuarte\u00a0CW,\u00a0Black\u00a0AW,\u00a0Murray\u00a0K,\u00a0et al. Validation of the Patient-Reported Outcome Mortality Prediction Tool (PROMPT). \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EJ Pain Symptom Manage\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2015\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E241\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E7.e6\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1016\/j.jpainsymman.2015.02.028\u0022\u003Edoi:10.1016\/j.jpainsymman.2015.02.028\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=25891663\u0022\u003Epmid:25891663\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DJ%2BPain%2BSymptom%2BManage%26rft.volume%253D358%26rft.spage%253D241%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-38-1\u0022 title=\u0022View reference 38 in text\u0022 id=\u0022ref-38\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.38\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EBroady\u00a0H,\u00a0Lynn\u00a0J. The physician\u2019s responsibility under the new Medicare reimbursement for hospice care. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EN Engl J Med\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E1984\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E920\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E2\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1056\/NEJM198404053101412\u0022\u003Edoi:10.1056\/NEJM198404053101412\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=6366559\u0022\u003Epmid:6366559\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DN%2BEngl%2BJ%2BMed%26rft.volume%253D358%26rft.spage%253D920%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-39-1\u0022 title=\u0022View reference 39 in text\u0022 id=\u0022ref-39\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.39\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EGillon\u00a0R. Medical ethics: four principles plus attention to scope. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E1994\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E184\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E8\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.309.6948.184\u0022\u003Edoi:10.1136\/bmj.309.6948.184\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=8044100\u0022\u003Epmid:8044100\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253D184%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-40-1\u0022 title=\u0022View reference 40 in text\u0022 id=\u0022ref-40\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.40\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EZeng\u00a0C,\u00a0Ellis\u00a0JL,\u00a0Steiner\u00a0JF,\u00a0Shoup\u00a0JA,\u00a0McQuillan\u00a0DB,\u00a0Bayliss\u00a0EA. Assessment of morbidity over time in predicting health outcomes. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EMed Care\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2014\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E(\u003Cspan class=\u0022cit-supplement\u0022\u003ESuppl 3\u003C\/span\u003E):\u003Cspan class=\u0022cit-fpage\u0022\u003ES52\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E9\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1097\/MLR.0000000000000033\u0022\u003Edoi:10.1097\/MLR.0000000000000033\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=24561759\u0022\u003Epmid:24561759\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DMed%2BCare%26rft.volume%253D358%26rft.spage%253DS52%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-41-1\u0022 title=\u0022View reference 41 in text\u0022 id=\u0022ref-41\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.41\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EAustin\u00a0PC,\u00a0van Walraven\u00a0C,\u00a0Wodchis\u00a0WP,\u00a0Newman\u00a0A,\u00a0Anderson\u00a0GM. Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EMed Care\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2011\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E932\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E9\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1097\/MLR.0b013e318215d5e2\u0022\u003Edoi:10.1097\/MLR.0b013e318215d5e2\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=21478773\u0022\u003Epmid:21478773\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DMed%2BCare%26rft.volume%253D358%26rft.spage%253D932%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-42-1\u0022 title=\u0022View reference 42 in text\u0022 id=\u0022ref-42\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.42\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ESafari\u00a0S,\u00a0Baratloo\u00a0A,\u00a0Elfil\u00a0M,\u00a0Negida\u00a0A. Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EEmerg (Tehran)\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2016\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E111\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E3\u003C\/span\u003E.\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=27274525\u0022\u003Epmid:27274525\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DEmerg%2B%2528Tehran%2529%26rft.volume%253D358%26rft.spage%253D111%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-43-1\u0022 title=\u0022View reference 43 in text\u0022 id=\u0022ref-43\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.43\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C,\u00a0Robson\u00a0J,\u00a0Sheikh\u00a0A,\u00a0Brindle\u00a0P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2009\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Eb880\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.b880\u0022\u003Edoi:10.1136\/bmj.b880\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=19297312\u0022\u003Epmid:19297312\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Db880%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-44-1\u0022 title=\u0022View reference 44 in text\u0022 id=\u0022ref-44\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.44\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C. Predicting the risk of chronic Kidney Disease in men and women in England and Wales: prospective derivation and external validation of the QKidney Scores. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMC Fam Pract\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2010\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E49\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1186\/1471-2296-11-49\u0022\u003Edoi:10.1186\/1471-2296-11-49\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=20565929\u0022\u003Epmid:20565929\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMC%2BFam%2BPract%26rft.volume%253D358%26rft.spage%253D49%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-45-1\u0022 title=\u0022View reference 45 in text\u0022 id=\u0022ref-45\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.45\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C. Predicting risk of osteoporotic fracture in men and women in England and Wales: prospective derivation and validation of QFractureScores. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2009\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Eb4229\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.b4229\u0022\u003Edoi:10.1136\/bmj.b4229\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=19926696\u0022\u003Epmid:19926696\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Db4229%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-46-1\u0022 title=\u0022View reference 46 in text\u0022 id=\u0022ref-46\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.46\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ECollins\u00a0GS,\u00a0Mallett\u00a0S,\u00a0Altman\u00a0DG. Predicting risk of osteoporotic and hip fracture in the United Kingdom: prospective independent and external validation of QFractureScores. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2011\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ed3651\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.d3651\u0022\u003Edoi:10.1136\/bmj.d3651\u003C\/a\u003E\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=21697214\u0022\u003Epmid:21697214\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Dd3651%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-47-1\u0022 title=\u0022View reference 47 in text\u0022 id=\u0022ref-47\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.47\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ECollins\u00a0GS,\u00a0Altman\u00a0DG. External validation of QDSCORE(\u00ae) for predicting the 10-year risk of developing Type 2 diabetes. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EDiabet Med\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2011\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E599\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E607\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1111\/j.1464-5491.2011.03237.x\u0022\u003Edoi:10.1111\/j.1464-5491.2011.03237.x\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=21480970\u0022\u003Epmid:21480970\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DDiabet%2BMed%26rft.volume%253D358%26rft.spage%253D599%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-48-1\u0022 title=\u0022View reference 48 in text\u0022 id=\u0022ref-48\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.48\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EMajeed\u00a0A. Sources, uses, strengths and limitations of data collected in primary care in England. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EHealth Stat Q\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2004\u003C\/span\u003E;(\u003Cspan class=\u0022cit-issue\u0022\u003E21\u003C\/span\u003E):\u003Cspan class=\u0022cit-fpage\u0022\u003E5\u003C\/span\u003E-\u003Cspan class=\u0022cit-lpage\u0022\u003E14\u003C\/span\u003E.\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=15615148\u0022\u003Epmid:15615148\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-49-1\u0022 title=\u0022View reference 49 in text\u0022 id=\u0022ref-49\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.49\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EHippisley-Cox\u00a0J,\u00a0Coupland\u00a0C. Development and validation of risk prediction equations to estimate survival in patients with colorectal cancer: cohort study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2017\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ej2497\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.j2497\u0022\u003Edoi:10.1136\/bmj.j2497\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=28620089\u0022\u003Epmid:28620089\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Dj2497%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-50-1\u0022 title=\u0022View reference 50 in text\u0022 id=\u0022ref-50\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.50\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ECollins\u00a0GS,\u00a0Altman\u00a0DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2012\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ee4181\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.e4181\u0022\u003Edoi:10.1136\/bmj.e4181\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=22723603\u0022\u003Epmid:22723603\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253De4181%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-51-1\u0022 title=\u0022View reference 51 in text\u0022 id=\u0022ref-51\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.51\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ECollins\u00a0GS,\u00a0Altman\u00a0DG. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2010\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003Ec2442\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmj.c2442\u0022\u003Edoi:10.1136\/bmj.c2442\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=20466793\u0022\u003Epmid:20466793\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMJ%26rft.volume%253D358%26rft.spage%253Dc2442%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-52-1\u0022 title=\u0022View reference 52 in text\u0022 id=\u0022ref-52\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.52\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003EFreund\u00a0T,\u00a0Gondan\u00a0M,\u00a0Rochon\u00a0J,\u00a0et al. Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: an observational study. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMC Fam Pract\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2013\u003C\/span\u003E;\u003Cspan class=\u0022cit-vol\u0022\u003E358\u003C\/span\u003E:\u003Cspan class=\u0022cit-fpage\u0022\u003E157\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1186\/1471-2296-14-157\u0022\u003Edoi:10.1186\/1471-2296-14-157\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=24138411\u0022\u003Epmid:24138411\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003Ca href=\u0022{openurl}?query=rft.jtitle%253DBMC%2BFam%2BPract%26rft.volume%253D358%26rft.spage%253D157%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx\u0022 class=\u0022cit-ref-sprinkles cit-ref-sprinkles-openurl cit-ref-sprinkles-open-url\u0022\u003E\u003Cspan\u003EOpenUrl\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Ca class=\u0022rev-xref-ref\u0022 href=\u0022#xref-ref-53-1\u0022 title=\u0022View reference 53 in text\u0022 id=\u0022ref-53\u0022\u003E\u21b5\u003C\/a\u003E\u003Cdiv class=\u0022cit ref-cit ref-journal\u0022 id=\u0022cit-358.sep20_12.j4208.53\u0022\u003E\u003Cdiv class=\u0022cit-metadata\u0022\u003E\u003Ccite\u003ESteventon\u00a0A,\u00a0Billings\u00a0J. Preventing hospital readmissions: the importance of considering \u2018impactibility,\u2019 not just predicted risk. \u003Cabbr class=\u0022cit-jnl-abbrev\u0022\u003EBMJ Qual Saf\u003C\/abbr\u003E\u003Cspan class=\u0022cit-pub-date\u0022\u003E2017\u003C\/span\u003E;Published online first 24 Jul:\u003Cspan class=\u0022cit-fpage\u0022\u003Ebmjqs-2017-006629\u003C\/span\u003E. \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1136\/bmjqs-2017-006629\u0022\u003Edoi:10.1136\/bmjqs-2017-006629\u003C\/a\u003E.\u00a0\u003Ca href=\u0022http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=28615343\u0022\u003Epmid:28615343\u003C\/a\u003E.\u003C\/cite\u003E\u003C\/div\u003E\u003Cdiv class=\u0022cit-extra\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003C\/ol\u003E\u003C\/div\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Ca href=\u0022https:\/\/www.bmj.com\/content\/358\/bmj.j4208.abstract\u0022 class=\u0022hw-link hw-link-article-abstract\u0022\u003EView Abstract\u003C\/a\u003E\u003C\/div\u003E \u003C\/div\u003E\n\n \n \u003C\/div\u003E\n\u003C\/div\u003E\n \u003C\/div\u003E\n\u003C\/div\u003E\n\u003C\/div\u003E\u003Cscript src=\u0022https:\/\/www.bmj.com\/sites\/default\/files\/js\/js_am8GAfrhW3uXc7HVCmqF3MltMmNunvhyJ6MM_6EffRE.js\u0022\u003E\u003C\/script\u003E\n\u003C\/body\u003E\u003C\/html\u003E"}